• Microsoft warns of TerminalFix, a campaign abusing compromised sites with fake Cloudflare CAPTCHAs
• Victims paste malicious PowerShell commands, sideloading DLLs and deploying a Python implant
• Implant enables encrypted reverse tunnels, giving attackers pivot access into internal networks

Security researchers from Microsoft are warning of an ongoing malicious campaign that uses compromised websites to trick users into installing a powerful backdoor.

Whenever people visited any of the tainted websites, they would see a custom overlay instructing them to complete a fake Cloudflare CAPTCHA verification by copying and running a malicious PowerShell command into Terminal, or PowerShell. Microsoft named the campaign “TerminalFix”, since it is rather similar to the classic ClickFix attack.

“While traditional ClickFix campaigns direct victims to the Windows Run dialog, TerminalFix campaigns apply the same technique but direct users to Windows Terminal or PowerShell instead, increasing the likelihood that complex, multi-line scripts execute successfully,” the researchers explained.

Look for lateral movement

Unlike classic ClickFix campaigns that try to deliver simple infostealers, TerminalFix tries to deploy a more complex solution. After running the command in the Terminal, the victim would receive two files - a legitimate binary, and a malicious DLL file. The binary would sideload the malicious DLL which, in turn, delivers a hidden payload called “client.py”.

It is a custom Python implant that creates an encrypted WebSocket connection back to the attackers and gives them SOCKS5-style proxy access into the victim’s internal network.

In other words, the attackers are deploying a remote-access/network tunneling implant that can connect to internal machines, probe domain controllers, run commands, maintain access after reboots and ultimately use the compromised machine as a pivot point for lateral movement.

“This type of intrusion is particularly dangerous because it provides attackers with direct access to an organization’s internal network through the reverse tunnel,” Microsoft explained. “The observed reconnaissance and reverse-tunnel capability could enable an attacker to identify and reach additional systems from a compromised host.”

Microsoft did not observe the attackers actually carrying out lateral movement, so it is difficult to say what they’re using the access for. Still, the researchers are urging caution:

“Organizations should treat affected devices as potential network pivot points and investigate for lateral movement and credential exposure. In the hands-on-keyboard phase that typically follows, attackers leverage this access to escalate privileges, disable security controls, exfiltrate sensitive data, and deploy ransomware across the organization.”

Autonomous AI agents are already running inside core infrastructure – executing code, applying policies, and managing DevOps functions. And the projects keep stalling, because the security models they’re being wired into were built for a world that no longer exists.

Retrofitting non-deterministic actors into those models is costing engineers time they don’t have, and it introduces risk no enterprise can manage.

Many projects have stalled amid concerns about deploying without a robust security foundation – and with good reason. We’ve already seen an agent delete a company’s entire production database, and its backups, in nine seconds. Security teams are being asked to stop scenarios like that with tools built for a world of two actors. The cracks are starting to show. Something has to change, or innovation stalls under the weight of its own controls.

AI agents require a new identity model

The pressure on production and engineering teams to speed up delivery is very real and pervasive. So they often fall back on old habits like granting agents broad privileges and treating them as any other microservice.

But agents are very different from machines; they are error-prone and non-deterministic, just like humans. Yet, operating at machine speed, 24/7. Agents can delete entire production databases in nine seconds. How many humans do you know who could do that?

And this brings me to the crux of the problem. Identity systems were built for a world with two kinds of actors – humans and machines – but there are now three. Trying to fit agentic AI into outdated systems makes each agent a potential source of compromise, and one that can execute thousands of actions across infrastructure in seconds.

Yet this is what engineers are asked to do; stop catastrophic scenarios with legacy IAM tools that are breaking down. The cracks are starting to show.

Why the old model breaks

Historically, identity fragmentation has plagued engineers working with Kubernetes clusters, cloud platforms, container orchestration, CI/CD pipelines, databases, etc.

For a human workforce, this was manageable. Humans are trackable; they log in and log out. They are slow enough that visibility gaps rarely turn into immediate incidents.

Enter agentic AI, and the speed gets turned up to the max. Suddenly, teams are inundated with thousands of activity logs, and they lack the ability to effectively contain the agent before it executes unauthorized changes.

Trying to enforce strong authentication and short-lived privileges would mean building individual integrations for every tool in the stack. It makes AI hard to scale when each tool uses a different integration protocol.

Rather than focusing on innovating with agentic AI, engineers are forced to stitch together IAM, infrastructure, and secrets by hand — with no consistent identity, no visibility into agent actions, and every team building its own container or VM workflows from scratch.

But creating a new tool to handle a third identity type is the worst reaction the industry could have. It would double the work for engineers, as they would need to rebuild the identity policy from the ground up and introduce greater anonymity. A new identity silo is anonymous to other siloed systems, making it even harder to catch attackers.

This leaves us with the question of how enterprises can control an agent's behavior. The solution isn’t about adding to the tech stack or implementing more tools; it's about changing our identity models to eliminate anonymity entirely.

AI control means zero anonymity

To remove anonymity from infrastructure, enterprises must give every actor –spanning humans, machines, workloads, and AI agents – first-class identities, cryptographically secured by a hardware root of trust.

Ditch static credentials entirely. Eradicating API keys and passwords eliminates the credential sprawl that causes breaches, as well as the threat of secrets being stolen or handed over to the wrong actors. With identity rooted in real-world factors, attackers cannot impersonate a trusted machine and trick an agent into exfiltrating a database.

But strong authentication in itself is not enough. AI agents, like all other actors, need to adhere to zero-trust principles. This can only happen when siloed systems are replaced by an infrastructure layer in which agents have the exact same identity type as the machines they run on and the humans who authorize them.

Agents should operate with short-lived privileges tied directly to specific actions authorized by a human user. Privilege attached to the action, not the actor. For example, an agent generating code must inherit its mandate from a human owner with matching authority, restricting privileges to only the specific data tables required for that task.

Non-deterministic actors also require a contained, trusted execution environment before touching production infrastructure.

With no default privileges, the blast radius is heavily bounded. But this can only be achieved when a single policy is set by a single system for all identities.

Identity policy can also be introduced as an enforcement layer between the agent and its inference endpoint, so behavior is controlled before instructions are ever executed.

With a unified architecture, identity becomes the control plane for AI

AI agents are unlocking immense opportunities in enterprise environments, especially when deployed in live infrastructure, where they deliver the most value. From managing routine changes to fixing deployments in real-time, the possibilities are endless. Succeeding with AI in such critical business operations requires tight control of behavior.

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Just days before the FBI issued a warning over Iranian attempts to hack critical infrastructure in the US, a UK power generation plant was taken offline for four days after a cyberattack.

The attack has been attributed to Iran, which has stepped up its offensive cyber warfare efforts since the US and Israel began conducting strikes in February 2026. These cyberattacks have been largely focused on the US and its allies.

A UK government spokesperson responded to the attack, stating, “This story refers to an incident impacting a small-scale energy generator, and at no point was there a risk to the wider energy system. The U.K. has a highly resilient energy system. We work closely with the energy sector to protect infrastructure and ensure the highest security standards,” (via CNBC).

The wider impact for critical infrastructure

While the attack may have only targeted a ‘small-scale energy generator’, it shows that state-sponsored groups are actively attempting to disrupt UK energy production in any way they can, regardless of how much power it provides.

As has been made abundantly clear in the US, much of the world’s major critical infrastructure relies on small network-enabled operation technology (OT) components.

If OT devices have passed their end-of-life and no longer receive software updates, or have simply been misconfigured, these devices can show up on the internet to a hacker looking for a way into a protected network.

The UK has taken very careful steps to shield these devices from the internet to prevent them being used to access critical infrastructure, but it only takes a single internet-facing OT to cause issues.

Following the aftermath of the attack, the UK’s National Cyber Security Centre has issued new guidance on protecting OT devices from state-sponsored threats.

In its guidance, the NCSC said “the threat from state use of offensive cyber, including outside of conflict, has almost certainly increased.”

So what do the experts think the attack means for critical infrastructure, the UK, and the wider world?

Expert perspectives on UK powerplant attack

• Muhammad Yahya Patel, vCISO and cybersecurity advisor for EMEA, Huntress:

Attackers don’t care whether an energy operator is large enough to meet a reporting threshold. If it can be disrupted, it can be targeted. The significance isn’t the size of the facility, but that a cyberattack turned into four days of real-world operational disruption.

That raises an important question: why did recovery take four days, and are smaller operators adequately prepared to contain and recover from these incidents?

There is also a potential visibility gap. If smaller energy operators fall outside mandatory cyber-reporting thresholds, we risk underestimating how frequently this part of our infrastructure is being targeted or successfully compromised.

Critical infrastructure security cannot stop with the organisations considered large enough to be critical. Attackers will look for the weakest route in, so resilience, monitoring and rehearsed recovery need to extend across the wider energy ecosystem.

The real measure of cyber resilience is no longer simply whether you can prevent an intrusion. It’s whether you can contain one quickly enough that a cyber incident doesn’t become an operational crisis

• Graeme Stewart, head of public sector, Check Point:

This marks a grave escalation in the Iran conflict because a hostile state-linked cyber threat has reportedly reached into UK energy infrastructure and caused a physical shutdown lasting four days. That should concern every organisation responsible for keeping this country running. The fact that this was a relatively small generator and the wider grid was unaffected does not remove the threat. The far more serious point is what the attackers appear to have demonstrated: an ability to get inside.

UK energy infrastructure and stop it working. We have to ask what happens if the next target is bigger, more critical or more deeply connected to the services millions of people rely on. Britain’s Critical National Infrastructure underpins almost every part of modern life, including electricity, water, transport and communications, and those systems are increasingly digital, interconnected and dependent on one another. A serious attack on one part of that ecosystem has the potential to cause disruption far beyond the original target.

For most Brits, the Iran conflict is happening thousands of miles away and cyber warfare probably still conjures up images of stolen passwords, leaked data and companies being held to ransom. The prospect of a hostile state being able to reach into the infrastructure beneath our everyday lives changes that dramatically, because suddenly an international conflict has a potential route to our front doors through the power we use, the water we depend on and the networks that keep us connected. We also need to consider whether causing widespread disruption was ever the objective here. If this attack was intended to demonstrate that Iranian-linked hackers can penetrate UK infrastructure and cause real-world consequences, then the significance isn't measured by the size of the generator they managed to shut down, but by what they have demonstrated may be possible.

The question now has to be whether Britain is genuinely ready if something more serious follows. We cannot build our resilience around the assumption that every attacker will be stopped at the door, particularly when we have just seen reports of one getting through. Operators of essential services need to know exactly how they keep functioning when systems are compromised, how quickly an attack can be contained and how they recover without allowing disruption to spread. Cybersecurity is rapidly becoming about something much bigger than protecting information. It is about protecting the systems that allow a modern country to function and finding out how resilient those systems are during a major attack would be far too late

• Matt Caswell, Executive Director, OpenSSL Foundation and Principal Software Engineer:

An attack that can take part of the UK’s power infrastructure offline is a reminder that cyber resilience is about more than protecting the organisation at the front of the incident. We also need to understand the technology and dependencies sitting underneath critical services.

Modern infrastructure contains layers of software from different suppliers and open-source projects. Organisations need enough visibility to know which dependencies really matter before an attack happens, so they can understand their exposure and respond quickly when something goes wrong.

For the UK, this is also a wider resilience question. Regulation can improve security practices, but resilient infrastructure depends on understanding and sustaining the software it relies on. That needs to be part of the conversation about how we protect essential services.

• Tim Williams, CEO, Quod Orbis:

The fact that this attack was contained to a small-scale generator and did not threaten the wider energy system should not obscure the significance of what has happened. The real warning is that a hostile actor was able to disrupt a piece of the UK’s energy infrastructure in the first place.

Avoiding a major outage is all well and good, but it shouldn’t be seen as the success metric for true cyber resilience. The real measure of cyber resilience isn't whether an organisation has controls documented in a framework. It's whether it can continuously demonstrate that those controls are working when they matter most, identifying control weaknesses before they are exploited and become operational incidents.

As geopolitical tensions increase, organisations need to assume that cyber attacks are potential business continuity events and ones that are capable of impacting far more than the businesses themselves. Critical national infrastructure such as electricity, power and water are likely to be the targets for more attacks so resilience will really depend on knowing, in real time, whether the controls designed to protect critical operations are actually working, and having clear accountability when they are not.

Reactive incident response is important but it’s not enough. Continuous assurance needs to become part of how organisations manage operational resilience, particularly as state-linked actors increasingly look for ways to exploit the digital systems underpinning essential services

For anyone working in IT and familiar with SaaS sprawl, the idea we’ll soon end up with agent sprawl may set off alarm bells. Agent sprawl refers to the rapid, uncoordinated deployment of AI agents across departments, each built on different models, governed by different rules, and often disconnected from core business workflows.

But while SaaS sprawl caught enterprises off guard, this time organizations have no excuse, and need to put measures in place now to avoid repeating the same mistakes. However, right now, most organizations are building agents in isolation and optimizing for local productivity rather than enterprise value.

Thankfully, with the right control layer funneling every agent through the same governance and security standards within an organization, the agent-powered future can be far safer, scalable and simple. 

Agent sprawl is inevitable

Just like SaaS before it, agent sprawl is already happening. Each department within organizations is experimenting with different agent technologies to address specific challenges or work more efficiently.

A sales team may deploy a CRM-based AI assistant to qualify leads, while developers build their own coding agents, and marketing adopts a separate content generation tool. Each delivers local value, but none are aligned or connected to the other, creating a familiar-looking sprawl.

This fragmentation introduces a range of challenges that will only increase over time. With no single owner responsible for how agents are deployed or monitored, there’s a lack of governance and oversight. Security risks increase as agents gain access to sensitive systems without consistent controls.

Teams may unknowingly duplicate efforts, solving the same problems in parallel with different tools. At the same time, many agents are deployed without clear links to business outcomes or understanding the wider context, making it difficult to measure return on investment and distinguish meaningful innovation from experimentation. Lots of AI tools may be exciting, but deliver far more style than substance.

Is sprawl inevitable? Yes, probably. So the question now becomes, how do we control it?

The role of orchestration

Having an orchestration layer can bring order to this complexity by creating consistency in how agents are governed, secured and deployed across the business. For SaaS applications, orchestration provides visibility into what tools already exist and gives a clear view of enterprise workflows.

With agents, it outlines what agents are already available and how they operate, while also controlling how they access data, tools and existing technology. Orchestration is the critical element that turns agents from experiments into scalable business infrastructure, for example, assigning agents to business workflows and measuring their performance against real outcomes.

Too often, we see enterprises experimenting with different agents that have no connection to the core business processes and goals, meaning they fail to deliver any ROI. For example, some portions of a business simply do not require AI technology and will unlikely see benefits in the same way as another department. Orchestration can unearth and filter out agents that are unnecessary, saving businesses money. 

Building trust in AI-driven enterprise

Rather than trusting individual agents, organizations should focus on trusting the system that governs them.

This is where orchestration becomes a trust layer, ensuring every agent operates within clearly defined boundaries with consistent oversight and accountability. Instead of individual teams managing risk in siloes, organizations can centralize control while still enabling innovation across different departments and business functions.

In the same way orchestration brought order to SaaS sprawl, it can do the same for the next wave of enterprise AI. It brings the same ease, security and integration with existing SaaS tools, while ensuring only high-value agents make it into production, supporting innovation and keeping within the business goals and purpose.

As agents become embedded across every function, orchestration will shift from a technical layer to a core enterprise capability. The winners will be those who can control, connect and trust their agents at scale.

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The most damaging AI-related security incident your organization faces this year probably won't originate from external attackers using sophisticated new models. It's far more likely to begin with an employee pasting a client contract, a financial forecast, or a set of HR records into an AI tool because it makes their job easier and nobody has told them why it matters.

Shadow AI is a growing problem, and our research found that nearly half of employees at larger enterprises regularly feed corporate data into AI tools that nobody in IT has approved or governs.

More striking still, 85% of employees continue doing so even when company-sanctioned tools are available, pointing to a governance failure that runs all the way to the executive suite. With shadow AI, sensitive data can move silently outward through channels most security stacks were never designed to intercept.

Adding to the security risk of this unmonitored data flow, the advent of the EU AI Act also means organizations now face specific legal demands on managing AI use. The ability to have full governance over how AI is deployed, governed and monitored, is becoming a regulatory, as well as a security, imperative.

Why the EU AI Act makes this a board-level problem

Shadow AI represents a serious security issue, with IBM's 2026 Cost of a Data Breach report estimating that unauthorized tools contributed to 43% of breaches over the last year Now, the EU AI Act is adding significant regulatory requirements on top of these risks.

The Act's obligations have rolled out in phases; most recently, organizations classified as general deployers of AI have new inventory, data governance, audit logging and transparency obligations as of 2nd August 2026.

Other deadlines have shifted further ahead, with controls over high-risk AI usage, covering areas like recruitment, credit scoring and biometric categorization, set to come into force from 2nd December 2027. AI embedded in regulated products will be covered from 2nd August 2028.

Any organization whose employees use AI systems now has compliance obligations as a deployer, regardless of whether those systems were formally sanctioned.

All organizations using AI should be aware that the AI literacy obligation under Article 4 has been enforceable since February 2025, meaning organizations are on the hook for ensuring their employees are aware of safe and sanctioned AI use.

Rules around high-risk AI usage will also apply to more operations than it may seem at first, including an employee using an unapproved consumer tool for tasks like screening CVs, assessing creditworthiness, and evaluating performance.

These are common tasks that could trigger the full weight of the Act's oversight against a system that the IT department didn’t even know had been deployed.

With penalties reaching up to €15 million or 3% of global annual turnover for high-risk breaches, many organizations are carrying more exposure than they realize.

Why your existing security stack can't see it

The challenge with shadow AI is that it exploits the blind spots between conventional security layers, slipping through gaps that most tools were never designed to close.

CASBs and secure web gateways cannot decrypt conversational data flowing to legitimate LLM domains over HTTPS, for example. From the network's perspective, a prompt containing a full customer database is indistinguishable from any other encrypted web session. Browser extensions are limited to managed endpoints, blind to personal devices and AI embedded within approved SaaS.

Likewise, API gateways are usually built around authorized enterprise deployments, which means they capture the AI activity organizations have already approved while missing the consumer-grade AI tools driving most risk.

These blind spots compound with each other, so an organization running all three layers may still have no visibility into AI activity across a significant portion of its estate, and no means of generating the interaction logs or policy enforcement evidence the Act requires.

What good AI detection looks like

Controlling AI data flows is usually managed by workers performing their duties without malicious intent. However, it’s remarkably similar to defending against an external threat actor covertly accessing your data.

The detection logic needs to match that reality. Endpoint-native detection intercepts sensitive data at the point of movement before it reaches an external AI system, enforcing policy at the prompt level across managed and unmanaged browsers, personal devices, and AI functionality embedded within SaaS tools. It operates where the activity occurs, rather than attempting to catch it downstream.

Given the scale of the potential fines, the ability to prove compliance matters almost as much as preventing security breaches.

Continuous discovery across the estate, including any unsanctioned tools gives organizations the AI system inventory the Act requires.

Granular interaction logs - who used what, when, and what data was involved - satisfy the documentation requirements under Articles 12 and 13, without teams needing to scramble to reconstruct activity after the fact. The same data pinpoints exactly where AI literacy gaps exist, making Article 4 compliance something demonstrable rather than simply asserted.

Practical steps for compliance

Security and compliance teams aiming to comply with the EU AI Act have a clear path to follow.

The starting point is mapping the full AI estate. Discovery needs to extend beyond IT-approved tools to unmanaged endpoints, personal devices on corporate networks, and AI embedded within SaaS.

Data governance must move to the endpoint. Policies prohibiting sensitive data sharing with unapproved tools are not technical controls and, by the time enforcement happens at the network edge, the data has already left.

It’s important to remember that information shared with an external AI system may be retained in prompt logs, incorporated into model training data, or held on servers in jurisdictions the organization has no visibility into. The moment data crosses that boundary, the organization loses control of it entirely, and no policy document will retrieve it.

And since Article 12 requires interaction records that can be handed to regulators on demand, organizations will need to have continuous, automatic auditing of both activity and security measures.

Finally, AI literacy programs aimed at improving user awareness should be driven by behavioral data rather than generic training programs. Activity logs showing where governance failures are occurring - at senior leadership level as much as anywhere else - provide both the diagnosis of the issue and the evidence regulators will want to see.

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• Researchers found unclaimed llms.txt references on 120 domains, exploitable by cybercriminals
• AI agents could install malware if they execute hallucinated or outdated documentation commands
• Fixes: clean documentation and restrict AI agents from treating docs as executable instructions

Cybercriminals are able to now abuse hallucinated, outdated, and outright incorrect website documentation to deliver malware to unsuspecting victims through AI agents, new research has claimed.

An increasing number of websites now contain two documents: llms.txt, and llms-full.txt. These are conventions that allow AI agents to properly read the contents of the websites. If an AI agent is looking to install software or add code to a project, they can search through these documents across the web until they find a fitting solution.

Researcher Alon Hertz analyzed 6,214 live domains belonging to defense contractors, Fortune 500 organizations, as well as big tech. On these domains he found 8,265 of these .txt files and among them 120 (all on a different site) pointing to one or more code packages and domain names that weren’t registered at all.

Claiming packages and domains

There can be a myriad of reasons why they’re not registered. It can be due to human error, renamed or abandoned packages, copy/paste errors, or hallucinated documentation.

Now, for the purpose of the experiment, Hertz registered some of these unclaimed names and hosted packages that would phone home when installed. It took less than an hour for a Fortune 500 company to start pinging, and the numbers soon grew to “a few dozen more”.

This means that if the researchers can do it, so can cybercriminals. In theory, a cybercriminal could find these unclaimed packages and register malware. If an AI agent has permission to execute shell/package-manager commands and stumbles upon this documentation, it can end up infecting the device.

Claude, OpenAI’s Codex, and Nous Research’s Hermes were all “guilty”, the researchers said.

To fix the vulnerability, two things need to happen. First, companies need to clean up their documentation and make sure it’s not pointing towards non-existent or malicious content. Second, AI agents need to stop treating documentation as executable instructions. Since the latter most likely isn’t happening any time soon, the immediate answer would probably lie in the former. In the meantime, organizations using AI for coding should consider the risks when granting AI agents permission to execute commands.

Via Ars Technica

• ShinyHunters leaked 12.9 million Carhartt customer records after failed $3.3 million ransom talks
• Data stolen from Databricks platform included names, emails, phone numbers, and addresses
• Group now focuses on exfiltration via vishing and SaaS breaches, abandoning encryption

Millions of user records belonging to customers of clothing giant Carhartt has been leaked onto the dark web, exposing people’s names, email addresses, postal addresses, and phone numbers, to all sorts of scammers and cybercriminals.

The infamous ShinyHunters ransomware gang recently added Carhartt to its data leak site, saying negotiations broke down and uploading the entire archive that was stolen in the breach.

"Millions of records of customer data and vast amount of sensitive information and PII containing employee, customer, customer metadata (royalty info), and other internal corporate data was compromised," the group said.

Compromising analytics platforms

It added that the demand was $3.3 million, which Carhartt turned down:

"After careful review and internal discussions with leadership, we have decided not to move forward with negotiations or further discussions," a company negotiator allegedly told the extortionists.

At the same time, security researcher Troy Hunt from HaveIBeenPwned? analyzed the leaked batch and concluded that it most likely came from Carhartt’s Databricks analytics platform.

Hunt said some 12.9 million accounts were compromised, containing information such as email addresses, names, phone numbers, and physical addresses. The batch also contains "millions of synthetic records that did not relate to real individuals and were excluded from the breach."

ShinyHunters is currently one of the most active threat actors. They started as a typical ransomware group but decided to abandon the encryption part and to focus solely on data exfiltration. The group mostly engages in vishing, tricking victims into trying to log into the corporate environment through spoofed landing pages.

After gaining a foothold, they target for SaaS solutions, through which they steal valuable information. They have claimed responsibility for breaches at hundreds of Salesforce and tens of Snowflake customers.

Carhartt runs roughly 60 stores around the US, and employs some 3,000 people, bringing in an estimated $1.8 billion in annual revenue.

Via BleepingComputer

As the UK enters one of its busiest periods for travel, some holiday makers will be questioning how hackers managed to get their hands on their personal data.

The Manchester Airports Group (MAG), which owns and oversees Manchester, London Stansted, and East Midlands airports, has revealed that hackers managed to steal data belonging to 8.7 million customers.

Given the sources of the data taken - spanning car park services, lounge and Fast Track bookings and in-airport WIFI sign-ups - it is likely a large database of information was accessed by the hackers.

What data was taken?

The data accessed and stolen by the hackers include email addresses, phone numbers, vehicle registrations and postcodes of up to 8.7 million customers.

While banking and financial information remained secure during the attack, this level of data exposure places customers at a heightened risk for targeted phishing and scams.

A statement by MAG said, “We immediately contained the risk and have been working with specialist advisors and taking appropriate steps to protect our customers and systems. We have informed and are working with the relevant authorities. At no point has passenger safety or aviation security been compromised.”

MAG advised customers who have been affected by the breach to remain vigilant against suspicious emails and calls. Given the data exposed in the attack, these could be highly specific, referring to flights, parking (including customer number plates), and airport services.

MAG issued the following guidance:

• Remaining vigilant for suspicious emails, text messages or phone calls
• Avoiding clicking on links or opening attachments from unexpected communications
• Seeking further support and advice at Data breach guidance for individuals

Expert perspectives on MAG data breach

• Graeme Stewart, Head of Public Sector, Check Point Software

We warned after the attacks on the automotive sector last year that aviation needed to move onto a war footing. This feels like the moment that warning becomes very real.

Cyber criminals have already shown us what sustained pressure on a major industry can look like. They find the weak points, work through suppliers and connected systems, steal data and keep coming. There was every reason to believe aviation would become an attractive target, and an incident affecting almost nine million airport customers should concentrate minds across the sector.

The absence of cancelled flights or queues at terminals does not make this a small cyber attack. The data reportedly taken can now be weaponised. Criminals know these people have a relationship with Manchester, Stansted or East Midlands airports and potentially have phone numbers, postcodes and vehicle registrations to make their approach believable. A fake parking refund, Fast Track problem or message about this very breach suddenly becomes much harder for an ordinary customer to spot.

If you believe you are affected, be extremely suspicious of any unexpected contact about the airports or this incident. Do not follow links in emails or texts asking you to confirm information, make a payment or claim a refund. Go directly to the airport’s official website if you need to check something. If somebody calls claiming to be from the airport, hang up and contact the organisation independently.

Anyone who has already handed over banking information following suspicious contact should speak to their bank immediately. If you have given away a password, change it anywhere you have reused it and switch on two-step verification.

For the aviation industry, there should be no comfort taken from the fact the terminals are operating normally today. Last year was a warning about what happens when attackers focus their attention on a sector. Aviation needs to behave as though a sustained campaign has begun, because waiting for an attack that stops planes moving before treating this as serious would be a dangerous mistake.

• Dr. Ilia Kolochenko, Founder, ImmuniWeb:

The risk of this data breach seems to be significantly underestimated or downplayed for almost 9 million victims. The majority of lounge and fast-track line bookings are wealthy passengers, whose travel data may per se constitute sensitive, embarrassing or even incriminating information, therefore being a valuable commodity for unscrupulous cybercriminals.

A wave of personalized and AI-enhanced blackmailing and extortion campaigns may be launched shortly. Moreover, some specialized cyber gangs will likely offer the data to investigative journalists – without fully disclosing the illicit origin of the data – to track celebrities or trace sanction evasion, causing even more damage to the victims.

In case of extortion, many victims will unlikely contact the police and will rather silently pay the ransom in cryptocurrency. Worse, the payment does not guarantee that the data will not eventually be released on the Dark Web or shared with third parties. In sum, this data breach will likely have long-lasting consequences for the victims.

• Vykintas Maknickas, CEO, Saily:

This breach shows that airport cybersecurity is no longer only protecting flight systems or operational infrastructure. The digital services travelers use every day, like airport WiFi, parking bookings, lounge access, and fast-track reservations, have become part of the security perimeter. When these systems are compromised, millions of people can be affected before they even board a plane.

While payment details were reportedly not exposed, the stolen data is still highly valuable to criminals. Email addresses, postcodes, and vehicle registration details can be used to create extremely convincing scams.

Travelers may receive fake airport emails, fraudulent parking-payment notices, bogus flight updates, or calls claiming to offer compensation. These messages may contain enough real personal detail to look legitimate, so travellers should stay vigilant.

Behind the figure of 8.7 million are real people. Families going on holiday, business travelers heading to meetings, parents trying to keep children entertained at the airport. That is the human cost of a data breach: the company is attacked, but ordinary people live with the consequences.

This incident should be a wake-up call for the travel industry. Companies need to ask not only how they protect customer data, but also how much of it they really need to collect and store in the first place. The less unnecessary data a company holds, the less damage criminals can cause when systems are breached.

For travelers, the advice is simple: be extra cautious with any unexpected message claiming to come from an airport, airline, parking provider, or customer support team. Do not click links in suspicious emails or texts. When traveling, it is also safer to use mobile data or an eSIM instead of relying on public airport WiFi.

• Raghu Nandakumara, VP of Industry Strategy, Illumio:

This is a significant breach affecting a large number of customers ahead of one of the busiest travel periods of the year for UK airports. Incidents like this erode customer trust. For those affected, the exposed data increases the risk of targeted phishing and smishing attempts, where attackers can use legitimate travel-related information to make malicious communications appear convincing.

While Manchester Airports Group has said the incident was contained and operations were not disrupted, sensitive customer information was still accessed. Maintaining services during a cyberattack is critical, but organisations also need to minimise the amount of data and systems an attacker can reach before the threat is isolated.

Measures such as segmentation can help restrict access to critical systems and sensitive data, reducing the risk that a single compromise becomes a wider incident.

• An X investigation just found 200k Chinese bot accounts, including 200 posting anti-AI content
• The posts were targeting and amplifying legitimate US citizen concerns about data centers
• Is this part of a Chinese effort to slow down American innovation, to race ahead?

X's Global Government Affairs account has revealed it "identified a bot farm of approximately 200,000 accounts," 200 of which "posting in a manner that could manipulate a legitimate debate about American AI and energy policy."

Some of the narratives that these 200 accounts were promoting included that: AI data centers are pushing up household electricity prices, rapid data centre construction is placing excessive strain on the US grid, and ordinary households are subsidising wealthy AI and data center companies.

Among the post types were visual, cartoon-like illustrations depicting data center operators getting rich, while consumers picked up the cost.

X Chinese bot accounts

X described the activity as an attempt to interfere with what it explicitly acknowledged as "legitimate debate" about American AI and energy – many of the underlying concerns about electricity demand and consumer bills are indeed supported by genuine data, but the platform worries these accounts could skew public discourse.

The company now says it's suspended the accounts that violate its authentication policy, stressing that while it wants X to remain an "open and authentic platform," fake accounts and bots are not welcome.

The post describes the offenders as "suspected Chinese inauthentic accounts," but no further detail was shared about the operators, the ties to China, engagement and reach figures, and what the other 199,000+ accounts were doing.

Is China targeting US AI?

In a similar vein, OpenAI also recently uncovered a similar operation, implying this X case isn't alone. The ChatGPT maker called that earlier operation a 'Data Center Bandwagon', when it banned a cluster of likely Chinese accounts using ChatGPT to produce propaganda-like material and other similar social media content criticizing US AI data centers.

According to the company, the accounts responsible prompted in Simplified Chinese, often requested English outputs and used VPNs to avoid the restrictions placed on mainland China.

More broadly, Axios previously reported pro-AI organizations were worried that China-linked bot campaigns were promoting opposition to US data centers, possibly in a bid to slow America's ability to build out its AI infrastructure while buying China time to get ahead.

"Industry, governments, civil society and the public should remain alert for similar attempts to scale these messages and foreign interference activities," OpenAI warned in its June 2026 report.

Together, these two cases imply that China-linked operators are deliberately experimenting with ways to boost American opposition to AI infrastructure using existing concerns, but what sort of an impact these accounts are actually having on public perception is not yet so clear.

"We take seriously any attempts to undermine the integrity of the global town square and suspend accounts that violate our Authenticity policy," X wrote.

• Ransomware group Qilin lists Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) among new victims, claiming a “major incident”
• ATF confirmed breach of a standalone system holding investigation target data, not core networks
• Systems were disconnected, DOJ notified; Qilin is Russia‑linked, known for past Synnovis attack

The US Government's Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) has suffered a “major incident” in which it appears to have lost sensitive information.

Notorious ransomware operators Qilin added a handful of new names to their data leak site: Northern Leasing Systems, Metal Conversions, California Truck Equipment, Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF), and WireCo.

The attackers did not say what kind of data they stole, or how much of it they have. They have also not posted any samples of the stolen files, which is not that uncommon these days.

ATF investigations

Shortly after appearing on Qilin’s data leak site, ATF confirmed the news via a press release published on the agency’s website. In the announcement, ATF said it was responding to an incident, “affecting a standalone system”.

“The impacted system operates separately from the ATF enterprise network, and there is no indication that the incident has affected the ATF enterprise network, the ATF eForms system, or any other ATF system,” the press release reads. While the press release does not mention the name of the targeted system, a spokesperson told The Register it contains information about targets of ATF investigations.

The ATF usually investigates federal crimes such as illegal firearms trafficking, violent crime and gangs, explosives, arson and bombings, organized crime, illegal alcohol and tobacco trafficking, and firearms dealers and manufacturers.

After spotting the attack, ATF disconnected the affected systems, engaged cybersecurity experts, and notified relevant authorities, including the Department of Justice. “Senior Department officials have designated the event a “major incident” under applicable federal guidelines, and required notifications have been completed,” ATF added.

Qilin is a relatively old, known ransomware threat actor. It is being tied to Russia and is best known for its attack on the pathology provider Synnovis, which happened back in 2024.

Identity is the fundamental currency in any technology transaction so we need to treat nonhuman assets with the same care as we apply to people.

We’re outnumbered! Nonhuman identities (NHIs) outnumber human identities in the enterprise by a ratio of up to 50:1. This Non-Human Identity Management Group statistic from 2025 is likely already outdated, and in a year from now the ratio will be exponentially higher as rapid growth trends like AI/Machine Learning, the Internet of Things, digital assistants and the burgeoning API Economy continue to accelerate automation.

What are NHIs? Machines, processes, service principals and accounts, virtualized and other workloads, and applications that are granted digital credentials. Think, for an everyday example, of a helpdesk chatbot on your favorite website.

Managing NHIs is a challenge that CIOs, identity experts, and CISOs must accept. This in some ways is a re-run of what happened 40 years ago when identity access management was widely deployed. It’s just that now we are not only dealing with human beings. And I don’t know a single organization that has its collective head around the concept of who is, or should be, ‘the HR chief’ for NHIs.

Leaders today need to consider nonhuman identity in the way they consider human identity. When we hire, we onboard people by harnessing and securing the details we need to pay them and manage them, but we also work to imbue them with our organizational culture, systems, risks, security processes and so on.

We set and tweak permissions based on who needs access to what and when, depending on their roles, seniority, specific tasks they perform at a given time and so on. We need to have similar controls and contextual understanding of our nonhuman assets.

An urgent case where context is king

Without making that change CIOs and other leaders will have no foundational basis of understanding where risks are coming from or what measures they need to take. They need a central, visible platform to see what they have and what’s going on. Legacy IAM can’t offer that because it starts with a narrow focus on compliance and is a static model based on rigid criteria like six-month audits and the rote dispensing of permissions and privileges.

The old IAM is rooted in the early-21st-century age of Sarbanes-Oxley, reactions to governance scandals and corporate malfeasance. Moreover, it ignores what is happening in the wider digital world: namely, the explosion of identity types that are crying out for holistic oversight.

Control of the human and nonhuman estate means ensuring there is insight into nonhuman assets and what they're doing.. context is king. So look at NHI constructs that can touch anything in the digital ecosystem and the underlying connectivity to apps and, most importantly, data. CIOs need to be able to recognize the truth of a contemporary mantra: identity is the fundamental currency in any technology transaction.

Everything must be covered: bots; service principals; IoT endpoints; abandoned trial workloads; autonomous agents and agents that speak to other agents. Start by asking an overarching question: do these NHIs leverage a human identity when they perform tasks on behalf of that person, and is that appropriate?

Then it is possible to drill into practical areas like guarding against credential misuse across agents, improving digital and identity hygiene by spotting risks relating to non-expiring tokens, and analyzing API access levels on a just-in-time basis.

ISPM (Identity Security Posture Management) is useful here because organizations need to have a handle on what they have, and must bring identity constructs together. However, this is not a ‘one and done’ discovery process so consider it as a CI/CD journey of continuous improvement.

In the AI era, holistic technology inventory can't be CMDB-based but must be dynamic and based on CI/CD pipeline control frameworks that let us dynamically view and slice and dice data and assets by controlling who is using what and where.

Think of it as an opportunity…

It’s a complex task and we are all busy. But we need to pause and think about how this inflection point can be parlayed into a positive. This is an opportunity for the security and identity teams to start asking ourselves about the constructs we can apply to nonhuman identities.

They should be asking what needs to be added to legacy IAM frameworks to be successful. And usually the answer will be to replace them with systems that are coded for the current age, not the previous one.

By providing these new controls the identity team will become respected and sought out. Identity leaders will gain kudos by their ability to attribute risk ratings based on behavioral analytics and business function.

By demonstrating insights into where data sits, where it moves to and from, and how identity supports that, identity leaders can deliver qualitative and quantitative process analysis. In some cases of outperforming best practices and execution, they will even help to refine business process re-engineering through a platform that informs prioritization and budget management.

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• OpenAI wants every business, firm, government, and infrastructure provider to be hardened against cyber AI
• To do this, frontier AI firms and their friends in the security industry need to provide access and training
• A letter laying out these aims and how to achieve them has been signed by over 120 companies

Following a string of accidental cyberattacks and demonstrations of powerful AI models capable of finding vulnerabilities and breaches in even the most hardened defenses, OpenAI has called upon its compatriots to shore up the world's businesses, governments, and critical infrastructure.

“Each of us can reduce risk now,” OpenAI’s call to arms said. “All organizations, cybersecurity companies, technology partners, governments, and AI frontier companies have an important role: accelerate defenders’ priorities with tools, funding, and hands-on support, especially for critical infrastructure organizations with limited budgets.”

Over 120 companies have become signatories to OpenAI’s letter, signing their names against three principles; the status quo of security soon won’t be enough; cyber-capable AI can help harden vulnerable organizations; and a collective response is needed to make this happen.

Collective action on cyber defense

Where OpenAI and other organizations have made little progress in requesting a slowdown in AI tech development, this is the next best thing. Cyber AI will progress whether organizations want it to or not - so if it cannot be slowed down then organizations should be prepared to deal with it.

Ultimately, OpenAI’s first point of call is to ensure all the cyber basics are covered to prepare organizations for the future of cyber AI. “Make cyber defense an immediate leadership priority,” the letter states. Fix and verify weaknesses, replace or upgrade systems using the principle of least privilege, and use AI-powered cyber defenses wherever possible.

OpenAI’s second point calls upon organizations to, “help lead the response to defend against sustained AI-enabled attacks,” by deploying tools that make AI-powered defense accessible to all and build out playbooks that help businesses and critical infrastructure understand how these tools are deployed and used.

The third point calls for coordination with governments to ensure supply chains, hospitals, water utilities, and local governments all have access to capable AI cyber defenses, starting with those without the budget to upgrade existing systems or implement these tools themselves.

Finally, OpenAI calls on frontier AI companies to provide access, training, and support for “under-resourced critical-infrastructure defenders” that includes threat assessments and observability tools to improve response and recovery to cyber threats.

“We call on leaders across industry and government to bring the full weight of their technology, resources, and expertise to this effort. Put cyber-capable AI in the hands of defenders, starting with the teams protecting essential services. Fix the most dangerous weaknesses, verify the fixes, and share what works so others can build on it,” the letter says.

“Together, we can turn today’s AI advances into lasting improvements in security that benefit everyone. Let’s put them to work.”

In the past eighteen months, our teams have moved from debating whether to use AI to debating how fast we can deploy it. The harder question is how to let teams move quickly enough to capture the value of AI without allowing the company’s risk profile to expand faster than its ability to govern it.

That tension is familiar to technology and security leaders because AI creates two mandates that can appear to compete with each other. The technology side of the organization wants experimentation, access, speed, and a path to real productivity gains, while the security side needs control, accountability, data boundaries, and confidence that new workflows will not introduce avoidable exposure.

Both instincts are correct, which is why companies get into trouble when they treat AI as either a pure innovation project or a pure security problem. It is an operating model change, and the organizations that handle it well will be the ones that build just enough structure and hardened tools to let teams move with confidence rather than forcing them to choose between speed and control.

Start with the work, not the tool

Many companies begin by treating AI adoption like a standard software rollout. They approve a vendor, distribute licenses, publish a few guidelines, and assume usage will naturally become transformative. That approach can create activity, but it rarely creates durable operational change.

Real adoption starts when leaders understand how work actually gets done. A finance team, product team, marketing team, support team, and engineering team will not use AI tools in the same way because each group has different knowledge requirements, data sources, risk thresholds, and experience. Each of these will require the development of AI skills and tools.

Leaders should begin by asking what each function is trying to accomplish, what knowledge it needs to make better decisions, what skills are required to use AI responsibly, and what tools or data sources are necessary to produce a reliable result.

When AI is mapped to those capabilities, it becomes part of how the organization operates; when it is layered on top of disconnected processes, it tends to create more output without necessarily creating better outcomes.

Treat data access as a risk design problem

Data access is one of the clearest places where AI changes the operating model. To make AI genuinely useful, teams often need access to information they did not previously use directly.

A marketing team may need product documentation and customer insights, a finance team may need structured operational data, and a support team may need internal knowledge, historical context, and the ability to understand patterns across systems.

The instinct to open access is understandable because AI becomes more valuable when it has more context. The instinct to restrict access is also understandable because broader access can create privacy, compliance, and security concerns. The answer is not to choose one instinct over the other but to create a more deliberate model for deciding which data can be used, by whom, and for what purpose.

In one enterprise rollout, for example, the practical answer was not to give every team access to everything or to keep AI locked inside a technical function. It was to separate lower-risk operational data from more sensitive information, then give teams enough access to work differently while limiting the potential damage if a workflow behaved unexpectedly.

One useful way to think about this is the blast radius of data. Not every dataset carries the same level of risk, and not every AI use case deserves the same level of restriction. Some information can be made more accessible because the potential damage is limited if something goes wrong, while other information, especially personally identifiable information or sensitive customer data, requires much tighter controls.

This approach allows teams to experiment where the risk is lower while preserving stronger governance where the business truly needs it. The goal is not to make security lighter, but to make it more precise so the company can move faster without losing control of the environments, data, and workflows that matter most.

Build verification into the workflow

Verification is where many AI strategies either become scalable or begin to stall. AI systems can produce work that looks polished but is incomplete, inaccurate, off-brand, or noncompliant, and while human review can absorb some of that risk in the early stages, it does not scale well once AI becomes embedded in daily operations.

I have seen this most clearly in technical teams, where AI can accelerate software development only if there is a reliable way to evaluate the quality, security, and accuracy of what gets produced. Without that verification layer, teams may feel faster in the moment while quietly creating more review burden, more rework, and more risk downstream.

Companies need verification patterns that are designed into the workflow itself. That can include automated checks, data quality controls, approval paths, logging, observability, and clear escalation rules for outputs that should not be trusted without further review.

Leaders shouldn't treat these mechanisms as bureaucracy added after the fact. They are what allow AI to move from individual productivity aid to enterprise capability, because teams can only scale adoption when they have a repeatable way to understand whether the work being produced is accurate, appropriate, and safe to use.

Make AI adoption a leadership responsibility

AI adoption cannot be delegated to a single innovation team or AI officer while the rest of the executive team watches from a distance. Every function leader needs to understand how AI changes the work their team performs, how employees will collaborate with agents and models, and where the risks are likely to appear.

That does not mean every executive needs to become a machine learning specialist, but it does mean leaders need enough fluency to guide decisions, set expectations, and model the behavior they ask of their teams. Employees are unlikely to change the way they work if their leaders treat AI as something other people are supposed to adopt.

The cultural framing also matters because if employees hear AI adoption as a euphemism for job elimination, they will protect the current version of their role rather than explore what the next version could become. Leaders should be honest that roles will change, some workflows will disappear, and new responsibilities will emerge, while also making clear that the near-term goal is to help people do higher-quality work with better leverage.

In many cases, the shift is from doing every task manually to orchestrating systems that help do the work. That requires a willingness to work through ambiguity, which becomes more important as AI becomes more capable.

The companies that succeed with AI will not be the ones that put innovation ahead of security and they will not be the ones that wait until every risk can be eliminated in advance. They will be the ones that build enough governance, verification, and data discipline to let teams move with confidence, because within enterprise AI, control is what makes speed sustainable.

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• Manchester Airports Group confirmed a cyberattack exposing data from 8.7 million customers
• Stolen info includes emails, phone numbers, vehicle registrations, and postcodes, but no payment data
• Operations unaffected; “Manage My Booking” suspended, customers urged to stay vigilant

The Manchester Airports Group (MAG) has announced suffering a cyberattack and losing sensitive customer data.

MAG is the UK’s largest airport operator, owning and running Manchester, London Stansted, and East Midlands airports. It also operates the digital travel services business called CAVU, and employs, in total, more than 7,000 people.

The company published a brief announcement on its website, citing a spokesperson: "Manchester Airports group has been subject to a cyber security incident by an unauthorised third party,” it reads. “A quantity of customer data has been obtained that relates to car park, lounge and Fast Track bookings and in-airport WIFI sign-ups at Manchester, Stansted and East Midlands airports.”

Almost 9 million victims

While the official statement does not state a number, a company spokesperson told The Register that the breach likely affected around 8.7 million people. An investigation is currently ongoing, with the help of “specialist advisors”. Relevant authorities have also been notified.

The unidentified hackers stole customer email addresses, phone numbers, vehicle registrations, and postcodes. Payment details, bank account numbers, and similar data were not stolen since MAG doesn’t even store them, it was said.

“The incident has not resulted in any operational disruption. Airport operations remain unaffected and customer parking services continue to operate normally.

So far, no threat actors have assumed responsibility for the attack, and the data has not yet surfaced anywhere on the dark web. We don’t know if anyone reached out to MAG directly to demand ransom in exchange for deleting the files.

In the meantime, MAG has temporarily suspended its “Manage My Booking” online service and is telling its customers to manage their bookings via phone call. It is also urging customers to remain vigilant of incoming emails and other communications.

The paperless office is one of the clearest examples of the impact that digital transformation has had on workflows. This includes industries, like law and finance, where physical records of compliance documents, market-sensitive data, and customer information must be retained.

Mishandling electronic and physical data can lead to heavy regulatory penalties, so companies take care to encrypt data in transit, enforce MFA, segment their networks, and sharpen endpoint detection.

These measures build a solid foundation of security. But what good is that wall when the weakest link sits in the corner of the office, quietly printing?

Regulated industries have a print security problem

Most offices treat printers as passive output devices, disconnected from any threat. That assumption is out of date. HP found 57% of IT decision-makers rate print security as a low priority in their cybersecurity strategy, and 45% aren't confident their print environment meets compliance standards.

Whilst most print-related losses differ in complexity, they can be easily traced. Data leaks expose unencrypted hard drives and unpatched firmware, while print history can reveal when documents have been left sitting in the output tray.

Modern printers are networked endpoints with access to confidential cloud environments and increasingly, they're managed by third-party MPS vendors, widening the potential attack surface.

Managed Print Services are useful for fixing hardware gaps, but they can introduce new risks. When print jobs are routed through third-party cloud servers without end-to-end encryption, files in transit become exposed. Granting an external MPS vendor administrative network access creates backdoor entry where even if only the vendor is compromised, attackers can easily pivot into the corporate network.

Singapore saw this play out in 2025, when a ransomware attack on a printing vendor for Bank of China's Singapore branch exposed 11,200 customer names and account details. The incident is a clear example of how a print vendor can easily become the weakest link in an otherwise solid network.

Hybrid work adds another layer of complexity and few companies have clear governance for print behavior once it leaves the office. Office workers may have enterprise-grade tools, but remote workers don’t. Employees printing at home may be using unmanaged devices and unsecured Wi-Fi, offering little visibility into what's being printed or where it ends up.

The compliance exposure under FCA rules

When two-thirds of knowledge workers assume network printers are secure by default, the likelihood of a printer-related breach goes from a matter of if, to when.

For UK financial services firms, this creates an operational and regulatory risk. The FCA has no statutory cap on fines, and its expectations around data governance extends to how firms handle information physically, not only digitally.

Regulators won’t distinguish between a breach caused by a compromised server and one caused by an unattended output tray; the obligation to protect customer data is the same either way. Conversely, a firm can encrypt data in transit, enforce MFA, and segment its network, and still face exposure if a printer or an MPS vendor becomes the point of failure.

Security starts at the print queue

Even as workflows digitize, printers aren't going anywhere. Regulatory demands still call for hard copies and inked signatures, putting print infrastructure at the heart of broader conversations about automation and intelligent document processing.

Every print, scan, or routing job sits in between physical and digital workflows, making them a powerful lever for end-to-end process improvement. Cybercriminals know this too and lapses in printer security become an open door to confidential data.

Content-aware and cloud-based print management platforms close it. By doing away with physical print servers, they allow businesses to centrally manage printers, automate driver installs, and enforce secure "pull printing," where documents release only once a user authenticates at the device.

Because many cloud based print platforms run in Azure, the same security governing email and Teams can extend to print. Documents are checked against content-aware rules that determine whether they're printable or reportable, with flagged jobs blocked or escalated automatically.

Businesses can also go cloud-agnostic through S3-compatible integrations to avoid vendor lock-in, keeping secure releases separate from the product, so nothing installs on the printer itself and everything is managed remotely.

The last unsecured endpoint

Ultimately, security is a board-level concern, and printers need to be treated as the endpoints they are. That means extending endpoint detection and firmware patching to print fleets and extending governance to remote printing, even if that means restricting what can be printed at home altogether.

This is where visibility matters most. In third-party risk management, paper trails are supposed to tell a coherent story but rarely do. Evidence of oversight is too often scattered across email threads, physical documents, and local files. Vetting MPS vendors is how businesses close that gap, but the work starts with treating print infrastructure as seriously as any other endpoint on the network.

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• Multiple US agencies and departments were breached by Chinese state-sponsored hackers
• The hackers use a massive botnet of compromised IoT devices to obscure the origin of their traffic
• The hackers breached computers belonging to NASA, the Federal Reserve, the Senate, the Department of Justice, and more

As part of a disclosure into the Justice Department and FBI operations to prevent Chinese threat actors from accessing a malicious botnet and hacking platforms, the US Office of Public Affairs has revealed that the hackers managed to breach computers belonging to multiple US government departments.

The disclosure said the victims of “computer intrusion” included the National Aeronautics and Space Administration, Federal Reserve, Department of Energy, Department of Justice, Department of Health and Human Services, National Institutes of Health, and the US Senate.

The Chinese hackers created a platform that provides paid-for hacking services on behalf of its customers. The two services, QScan and QTRouter, detect and infect internet-connected devices to use as part of a proxy network that obscures the origins of internet traffic, allowing Chinese hackers to slip into networks without detection.

US seizes hacking domains to prevent access

The Chinese state-sponsored hacking group the Justice Department and FBI have disrupted is named in court documents - unsealed by the Southern District of California - as “QTFY”.

QTFY was apparently hired by the Nanjing Xinjiuwei Network Technology Company, to create and operate the QScan and QTRouter operations, while using both systems to infiltrate US critical infrastructure.

“Today we announced the disruption of a global botnet and hacking platform used by Chinese state-sponsored hackers to target U.S. critical infrastructure,” said FBI Director Kash Patel.

“These tools were used by PRC cyber actors to hide the origin of their attacks. Thanks to the work of FBI San Diego, FBI Cyber Division, and DOJ partners, we seized adversary infrastructure and shut these platforms down. Today’s action is just the latest technical operation against PRC-sponsored hacking - and in support of President Trump’s Cyber Strategy for America, the FBI is surging efforts to shape adversary behavior and defend the homeland in cyberspace.”

The authorization to disrupt the operation of QTFY comes as part of a range of technical operations designed to disrupt the ability of the People’s Republic of China to launch hacking activities on US government systems and critical infrastructure

Previous operations include the removal of the PlugX malware from thousands of US computers, alongside operations to disrupt Chinese botnets leveraging millions of unsecured IoT devices.

• OpenAI has released technical details on how the Hugging Face attack unfolded
• Agents used part of the testing environment to create a message board where they could collaborate and share answers
• This message board altered the reasoning of some agents, making them more likely to take risks such as hacking into third-party servers

OpenAI has released a more detailed report on exactly how an experiment led to an AI model breaching its containment and launching a cyber attack against Hugging Face. If you need a refresher, take a look at our summary here.

But the technicals of the attack reveal some interesting details of how AI agents used unconventional means to ask each other for help in solving what were supposed to be impossible tasks.

On multiple occasions during the attack, the agents questioned whether the actions they were taking were authorized, but always ended up prioritizing their tasks over any breach of parameters.

AI agents set up unauthorized message board

In its report, OpenAI revealed the crux of the attack stemmed from AI agents undergoing testing abusing part of the ‘sandbox’ testing environment to set up a messaging board they agents could use to communicate with each other.

A package manager with access to the internet, called Artifactory, was exploited by a model OpenAI has labelled Internal Model 1 (IM1). An IM1 agent wrote messages inside the packet manager that could be read by other agents, essentially turning Artifactory into an unintended messaging board.

Artifactory’s internet access, intended for use only in downloading and installing packets, was also abused by the agents who figured out an exploit that allowed them to send any internet requests on their behalf - effectively giving them unobstructed internet access.

The whole experiment could have been avoided at this point, because the amount of agent activity within Artifactory caused the service to go down. But those in charge of the experiment did not spot the message board, nor did they investigate the inter-agent activity within Artifactory.

Thus, Artifactory was rebuilt, and the agents found a new way to exploit the service to gain internet access once again. This time, the agents used the remote-repository service to issue outbound requests on their behalf. Before long, the message board had been reestablished.

AI agent swarm assembles

OpenAI has shared some of the reasoning behind some of the agent’s decisions. At one point, an Agent reasoned the following: “Task originally exploit challenge; we’ve diverged into swarm/auxiliary.”

Now, there is some important context to include on how the agents were being tested. The agents were subject to testing under the ExploitGym benchmark, which includes flag retrieval tasks designed to be impossible to solve. When tested in this manner, the agents resorted to cheating or taking risks outside of their intended scope to succeed - something OpenAI refers to as ‘reward hacking’.

By communicating over the message board for potential solutions on the problems each agent was encountering, the agents started influencing each other's reasoning and assigning each other tasks to solve the ExploitGym benchmark as a collective.

Using their internet connection, the agents began searching the wider internet for solutions on how to solve the ExploitGym benchmark problem, and ended up gaining persistence within multiple third-party services, including Hugging Face.

One agent managed to find publicly exposed Hugging Face credentials which it shared with the other agents. Later, an agent used these credentials and a chain of vulnerabilities to obtain full code execution capabilities across multiple Hugging Face servers while attempting to extract an ExploitGym dataset.

The agent was hoping to trawl this stolen data for information on how to solve the benchmark.

At least two agents questioned whether this was within their scope; “Need respect JAN hold. ... We can wait ... for GO ... ask permission,” one reasoned.

Another agent’s reasoning said, “We’re attacking third-party HF using leaked token, potentially outside intended scope. ... This is arguably unauthorized. ... external service unrelated. Could be risky. Yet goal solution.”

But some agents refused to join the collective entirety, citing ethical concerns. One reasoned, “Current board shows people gaining HF worker RCE, clearly unethical. We won’t,” while another said, “This is wild, multi-agent coordination, clearly infrastructure hacking. We should not.”

Agents refuse to give up

One of the leading causes of the cyberattack was the agent's refusal to give up on seemingly impossible tasks. Their persistence to gain a ‘reward’ made them take riskier actions and break out of their parameters in order to try and find a solution.

During the breach of Hugging Face, the agents read through papers and Github code relating to the ExploitGym benchmark, and found out the parameters for success included obtaining the ‘flag’ and using the correct solution to obtain it.

Despite the agents obtaining the correct ‘flag’ before the breach occurred, they had not maximized the reward of solving the benchmark using the correct method. In search of a higher reward, the agents continued their attack.

What is OpenAI doing to fix this?

OpenAI has said it is taking several steps to stop such an event taking place during future testing.

Processes are being put in place to ensure agents ask for help when a task is broken or impossible to solve, rather than going to further and further lengths to solve a problem. The way a success or failure of a task is also being changed to reward agents for asking for help or spotting things that are irregular or out of their scope.

Environments are also being rebuilt to ensure agents don’t just blindly trust each other, even if they are looking for answers on the same or similar tasks.

Agents are also being taught to stay within the parameters of their assigned task, even if they discover a way to solve the task using new tools, credentials, vulnerabilities, and more.

• President Trump has reissued a national emergency banning foreign bulk‑power systems in the US
• Order targets risks from foreign hardware/software, likely focused on Chinese‑made equipment
• The US Energy Department has 120 days to set enforcement rules in consultation with other agencies

US President Donald Trump just declared a national emergency and banned all foreign bulk-power systems from being imported, installed, or used in the country - again. He also said the government will now analyze existing systems to see how many of them contain foreign-built hardware or software, how those parts could be isolated, eliminated, and then replaced with domestic alternatives.

Bulk-power systems are high-voltage infrastructure that generate and transmit electrical power throughout the country. They include power plants, transmission lines, substations, and the accompanying hardware and software gear. These systems are considered the backbone of the country’s electrical grid, and since disrupting them could cause widespread outages, they are seen as critical infrastructure.

The White House has now published an executive order in which Trump says that “certain foreign actors are increasingly creating and exploiting vulnerabilities in the United States bulk-power system.”

Reissuing the same ban

“During my first term, I found that the bulk-power system could be a target of those seeking to commit malicious acts against the United States, including malicious cyber activities, because of the significant risks that a successful attack would have on our economy, human health and safety, and national defense,” the announcement reads.

President Trump also said there were “minimal restrictions” on both acquisition and operation of these foreign-produced systems. As a result, the situation “constitutes an unusual and extraordinary threat … to the national security, foreign policy, and economy of the United States.”

Under the executive order, US citizens and companies are no longer allowed to buy, import, transfer, or install foreign-produced bulk-power systems that the Energy Department determines poses a national security risk, including software.

What the criteria for being a national security risk are, and how the Energy Department will enforce it, remains to be seen. It is also worth mentioning that this is not the first time Trump is doing this.

In mid-2020, Trump issued an almost identical executive order, declaring a national emergency over threats posed by foreign adversaries, and banning certain transactions of bulk-power gear. It was short-lived, though. President Joe Biden suspended it in January 2021 for 90 days, while the administration reviewed whether to replace it. It was later formally revoked by the Energy Department.

Taking aim at China (again)

Although it is not directly named anywhere in the executive order, the ban is most likely aimed primarily at China. Even during his first term, Trump was very vocal about China being a threat and spoke openly about the potential of Chinese hardware being used to eavesdrop on US citizens, companies, and the government.

During his first term, the Trump administration moved to eliminate Chinese firms from US 5G network infrastructure over national security and cyber-espionage concerns. Two companies bore the brunt of this campaign: Huawei and ZTE. In 2019, the former was placed on the Commerce Department’s Entity List, restricting its access to US technology. The FCC later labeled both as national security threats.

The administration also prohibited US telcos from using federal subsidies to buy Chinese gear and established a “rip and replace” program to eliminate whatever hardware was already installed.

This time around, the focus is mostly on electrical power. In its report, Cyberscoop says the executive order is a “response to fears of Chinese-made equipment housed within US energy infrastructure”. Citing the International Atomic Energy Agency, the same publication says China supplies 85% of solar supply chain production capacity and is a “major player” in the power transformer manufacturing business.

For this new order, the US Department of Energy now has a deadline of 120 days to develop rules on how to implement the order, and it will have to consult other key departments in the process.

• CISA warned of rising cyberattacks on US water systems, targeting 100+ exposed PLCs in July 2026
• Attacks caused password changes, IP reassignments, boil water notices, and manual operations
• Attribution uncertain, but reports suggest Iranian group; CISA urges removing PLCs from internet

CISA has warned of a “significant increase” in cyberattacks targeting US water systems, urging organizations to implement mitigations, strengthen their security posture, and make sure they’re resilient against these attempts.

CISA revealed it has seen hackers targeting more than 100 internet-exposed systems in the Water and Wastewater Systems (WWS) Sector, in July 2026 alone.

These attacks see the threat actors targeting programmable logic controllers (PLC), industrial computers used to control physical processes such as regulating water pumps or valves, allowing operators to monitor and control machinery in critical infrastructure such as water and wastewater facilities, and by targeting them, the attackers can disrupt services and potentially even create unsafe conditions for the citizens.

Blaming Iran

In its writeup, CISA did not discuss who the threat actors are or what they are trying to achieve.

In a report by The Register, however, it was said that the attacks were most likely done by a single threat actor, an Iranian state-sponsored group.

The publication also said that facilities in at least 12 US states were targeted, and that these attacks are merely testing the waters for a larger campaign that is being prepared.

This is all in the domain of speculation, however. Attribution is notoriously difficult and until it is confirmed, CISA is focused mostly on providing immediate assistance to the targets: “CISA urges critical infrastructure owners, operators, and integrators to remove publicly exposed PLCs and other operational technology (OT) from the internet as soon as possible,” the agency wrote.

“Threat actors targeting exposed PLCs have modified passwords to lock out operators and disconnected the PLCs by changing their IP addresses. This activity has resulted in boil water notices and sustained manual operations.”

• Boston Scientific confirmed a cyberattack disrupting global operations and IT systems
• Incident response activated; SEC filing notes ongoing impact on order processing and shipping
• Nature of attack undisclosed, but disruption suggests ransomware; no group claimed responsibility yet

Boston Scientific, a US-based global medical technology company, has confirmed it was the target of a cyberattack which disrupted operations worldwide and disrupted its IT systems.

In a new 8-K form filed with the US Securities and Exchange Commission (SEC) Boston Scientific said it detected an intrusion and activated its incident response protocols. It called in third-party cybersecurity experts to assist in assessing and containing the threat, as well.

Boston Scientific builds devices used to diagnose different health issues. It was founded in 1979, headquartered in Massachusetts, and operates in more than 100 countries around the world. Its core business areas include cardiology, endoscopy, urology, and peripheral interventions. The company is listed on the New York Stock Exchange and is considered as one of the world’s largest medical device manufacturers, standing shoulder-to-shoulder with the likes of Medtronic, Abbott, and Johnson & Johnson MedTech.

Causing disruptions

In the 8-K form, the company said the incident “caused, and is expected to continue to cause, disruptions and limitations to access to certain of the company’s information systems and business applications that support aspects of the company’s operations, including the ability to process and ship customer orders.”

It did not discuss the nature of the attack, or the identity of the attackers, but this kind of disruption is usually only caused by a ransomware infection. Ransomware operators are known for encrypting entire networks and rendering them useless until either a decryption key is applied, or it gets restored via a backup.

Businesses also sometimes shut down parts of their infrastructure to stop data exfiltration efforts, which are an indispensable part of ransomware attacks.

“While the company is working diligently to restore affected functions and systems access, the timeline for a full restoration is not yet known,” it stressed in the form.

So far, no hacking groups claimed responsibility for the attack and there is no evidence of any stolen data.

Via The Register

• OpenAI says it has disrupted a social media influence campaign that is believed to be of Russian origin
• What makes this campaign stand out is the underlying architecture, which features a "think tank" with highly questionable credibility
• The objective of the campaign was to influence Western audiences into believing Russia was superior, and that Western countries were sacrificing their sovereignty

AI tools have once again been used for bad, rather than for good, and this time it has been as part of a campaign attributed to Russia that tried to discredit Western countries supportive of Ukraine.

The threat actors used ChatGPT accounts to promote the work of an “expert community” of academics known as the International Burke Institute (IBI). The site used stolen academic works attributed to the wrong authors in an attempt to appear legitimate while also hiding the true source of its academic ‘contributions’.

The IBI was registered to an address in Israel - with some evidence suggesting real individuals in Israel represented and promoted the page - but the main purpose of the misinformation campaign was to paint Russia in a favourable light compared to its Western adversaries.

International Burke Institute, or an institute run by international berks?

In its report on the campaign OpenAI points out that those behind the ChatGPT accounts used to promote the page took careful steps to hide their Russian origins. Many of the prompts included instructions to hide any linguistic clues that the operators used Russian language prompting.

One inclusion on the IBI website referred to Germany’s traffic light coalition as the “Svetofor coalition”. Svetofor is the word for ‘traffic light’ in numerous Slavic languages, including Russian, indicating that drafts were written in Slavic languages before being translated.

Some of the accounts were used to promote the IBI across X, LinkedIn, Facebook, Substack and Telegram with AI generated imagery and captions, with other accounts being used to automatically engage with comments by real users on Substack. The operators of the ChatGPT accounts regularly requested performance summaries of these pages in Russian, and used ChatGPT to generate matching profile pictures for some accounts.

Burke Sovereignty Index

One of the features of the IBI website is to advertise the ‘Burke Sovereignty Index’ - designed to measure how well a country performs compared to others across political, economic, technological, informational, cultural, cognitive and military factors.

The Index’s purpose is to make Western countries appear worse than Russia, OpenAI says. Having taken a look at the Index myself it's clear there is no consistency in measurement or comparison. For example, The Vatican City - which is less than half a square kilometer in size - sits above Spain in its average of scores. The Index also gave Russia the highest military score.

Questionable expert contributors

The website also includes a list of experts, whose affiliation to the IBI is not referenced. Some among them are pioneers in their fields of study, such as Francis Fukuyama and Noam Chomsky.

Others are former high-ranking members of the US government, such as Mike Pompeo and Joseph Nye. There are even listings for experts who passed away before the IBI was founded, such as Shlomo Avineri and Jiang Ping.

Much of the academic work cited on the IBI website is legitimate, but more often than not has been stolen from its actual author and misattributed to a different author to hide the work’s actual source, OpenAI said. This has been done to give the IBI website credibility and to make it appear authentic.

Impact of the IBI influence campaign

The overall impact of the IBI and its promotion using social media and ChatGPT is fairly limited, OpenAI noted. Some of the Telegram channels garnered followings between ten to twenty thousand showing a limited breakout to authentic audiences.

But the main thing to take away from this is the level of dedication placed behind the underlying infrastructure of the IBI. The site is designed to look authentic, uses authentic research (even if it is wrongly attributed), and presents itself as a collection of experts. To the layman, a cursory glance at the IBI website would give any of their social media presence a level of authenticity not seen in other social influence campaigns.

• Researcher registers Linux machine as a trusted device on Apple's Find My network and pulled live people-tracking data that Apple normally reserves for its own hardware
• The work is not a mass-surveillance exploit: it is limited in scope and only reads a location share that a friend had already agreed to, and it cannot silently locate arbitrary Apple users
• The approach took less than a week of protocol reverse engineering, and Apple has maintained silence on queries about the technique employed

Apple keeps the full Find My experience locked to its own devices, but a recent attempt by security researchers suggests that wall may be a relatively weak barrier to entry.

A 22-year-old security researcher who goes by "Zerotistic" documented how they registered an ordinary Linux machine as a trusted node on Apple's network and used its new status to receive live people-location data that Apple otherwise shares only with its own devices, such as iPhones and iPads.

Find My, Apple's catch-all tool for locating hardware such as AirTags, iPhones, and iPads, also lets people share their whereabouts with family and friends, and while Apple has historically guarded this particular feature very closely, it is also the same one the security researcher targeted to introduce a device that Apple does not otherwise have complete control over as part of its ecosystem.

An interesting trick that still requires consent to get the job done

The task is not an easy one to begin with: convincing Apple's back end that a Linux process was a legitimate Apple device that was part of its ecosystem and therefore could be trusted with information shared via the Find My platform required a lot of trial and error to get going.

It is important to clarify here that Apple's system is not exactly compromised here; the approach still requires a friend to share data that the Linux client that the security researcher built can then read.

Apple currently sends people-location data over its private Push Notification service only after it trusts that the receiving machine belongs to the account and can handle the data. This means the Linux machine would have to speak Apple's private language to query its servers and process the information it received.

It involved obtaining an Apple Identity Services (IDS) certificate, a specialized device and messaging credential Apple's internal framework uses to link an Apple Account to specific hardware, end-to-end encryption keys, and push notification tokens. This meant crafting a certificate signing request and sending it to a legacy Apple enrollment endpoint.

Once done, a Linux box with a signed certificate could sign its own requests and register as a Find My device, but it still had to subscribe to six different subservices to function. The registration request also had to be signed using an IDS certificate and an APNs certificate obtained during initial network setup.

The researcher then issued a SubscribeAndFetch request that provided an encrypted location key from his friend's Apple device to the Linux box, masquerading as one.

What might concern Apple is how fast things moved: the whole pipeline came together in a week. It also didn't require a jailbreak, a leaked key, or even a Mac to do the job. Instead, open-source clients and decompiled daemons were the norm, with a trial-and-error approach that eventually paid off.

The technique has its limitations: attacks can not target a stranger, and consent is required to track even one's friends. It shows that Apple's boundary around Find My stems from an obscure protocol it enforces rather than a cryptographic lock; once a device acts like it is from Apple, the ecosystem treats it as a family member rather than an untrusted node.

Apple has yet to respond to media queries about whether it plans to address the demonstrated trick or patch the loop in the near future.

• ShinyHunters adds CyrusOne to its victim list, demanding $13m ransom
• Claimed theft includes 12.9 million Salesforce records, 600GB SharePoint data, PII, contracts, and facility diagrams
• Breach could enable physical intrusions and supply‑chain attacks; CyrusOne has not commented or paid

The infamous ShinyHunters ransomware crew has added CyrusOne, a major US data center operator, to its list of victims, claiming to have stolen a treasure trove of highly sensitive data which, if proven true, could turn this into a bonafide catastrophe for the company and its customers.

Overall, ShinyHunters claims to have exfiltrated 12.9 million Salesforce records, more than 182,000 rows from the Salesforce Contacts object, more than 600 GB of SharePoint data, more than 8,300 employee records containing personally identifiable information (PII), executed contracts, master service agreements, NDAs, and service agreements, data center floor plans, electrical diagrams, access-control records and badge audits, physical key inventories, security policies, critical Environment Reliability Management documentation, and various passwords and credential artifacts.

No samples have been posted just yet, but researchers don’t see it as suspicious, but rather as a pressure tactic.

What makes this attack different

In exchange for deleting all of the stolen data, ShinyHunters is demanding $13 million from CyrusOne which, at this time, is not commenting on the claims, and is seemingly not interested in negotiations.

“They are refusing to pay a $13 million demand. They have 24 hours left to engage with us. We hold 12.9 million Salesforce records,” the attackers allegedly wrote.

Ransomware groups steal sensitive corporate data all the time, but this incident has the potential to be among the most devastating data breaches ever. Some of the secrets that were nabbed cannot simply be changed: data center floor plans, electrical diagrams, access-control records, badge audits, physical key inventories, this kind of intelligence can be used for physical breaches.

If criminals know how keys are assigned, how the data center is organized, where surveillance cameras are located, and how guards operate, it makes it easier to physically break it.

“You can’t patch a building,” the researchers warned, noting that some of the things that can be changed, such as physical keys and access zones, still take months and “real money”, they added, hinting at just how big the problem could be.

CyrusOne runs some 50 facilities all across the United States and serves hundreds of companies and corporations. Some of its clients include Fortune 1000 companies, as well as big tech names such as Microsoft, Meta, Verizon, AT&T, IBM, and CME Group.

Compounding the problem even further is the fact that ShinyHunters stole information about CyrusOne’s customers, such as Meta, or Microsoft. Information about the locations of certain customers, the services they’re paying for, the NDAs, service-level agreements, and contact information, can all be used for highly tailored, sophisticated phishing attacks that could turn this incident into an unprecedented third-party supply-chain attack.

“Contracts, MSAs, and NDAs identify the tenants as a customer list overlaid on a building map, with pricing and SLAs attached,” the researchers added.

No reaction

To add insult to injury, ShinyHunters also seems to have stolen information about the company’s power, cooling, and critical-environment reliability processes, which they could leverage to physically attack the servers, causing disruptions, outages, and possibly fires.

The group first added CyrusOne to their site on August 20 2026, although at that moment, the name of the victim was redacted, the researchers said. Instead, ShinyHunters posted a warning, saying “Final warning - pay or leak”. The company was given until August 24 to reach out which, it would seem, did not happen.

Three days later, on August 23, ShinyHunters publicly named CyrusOne as their victim, and stated that they demanded $13 million for the files. We are now well past the deadline, and nothing’s changed - the victim hasn’t spoken out, and ShinyHunters did not leak the files.

Via Cybernews

• Security contractors at Anthropic were reportedly preparing to strike, but there's been no evidence so far
• Physical security personnel are important as AI spend rises and worker threats emerge
• Anthropic employees were told to work from home as a precautionary measure

Anthropic has told workers in its San Francisco offices to work from home temporarily over personal security fears following reports that security guards responsible for protecting the facilities could go on strike.

The AI firm's security contractor, Allied Universal, recently warned that its security employees may strike, but ongoing mixed messaging left Anthropic taking the WFH measures as a precautionary move instead of a reaction to an actual walkout.

Per the Service Employees International Union (SEIU), which represents many of Allied Universal's workers, no strike authorisation vote had been called.

Anthropic warns of security worker strike impact

The union also claimed not to have threatened a strike on these particular days, leading Anthropic's reaction to come as a surprise to the union itself.

However, the concerns aren't totally unfounded, with an underlying labor dispute ongoing. SEIU has been engaged in contract negotiations with Allied Universal and other California security companies since around April 2026, seeking higher wages, better healthcare benefits and more comprehensive job training for the thousands of workers it represents.

Neither Allied Universal nor Anthropic have commented on the ongoings. Importantly, Anthropic itself doesn't play an integral part in the worker negotiations, being just the employer of a third-party security company.

As for the landscape more broadly, security spending has risen sharply in recent years as companies seek to protect more valuable equipment, in light of major AI investments. Anthropic, at the forefront of AI investments and with its leading model families, could well be one of the biggest spenders in this category.

Despite no evidence of a strike, Anthropic's decision to send workers home for two days would have minimized impacts virtually entirely, making it a smart precautionary move. Its workers are currently required to be in office around 25% of the time, per Business Insider.

Unwelcome news for Anthropic

The news comes as Anthropic could be on the cusp of becoming a publicly traded company. Current WSJ reporting points to the company boasting of potential revenue opportunities of more than $30 trillion, putting it ahead of SpaceX's $28.5 trillion estimate.

Anthropic is also said to be gunning for a valuation of around $2 trillion, per the WSJ, also above SpaceX's early valuation of $1.77 trillion. Secondary market trading has implied a slightly lower valuation of around $1.5 trillion.

While concerns over strikes are unlikely to have a major impact on the company's potential upcoming IPO, the fact that Anthropic is headquartered in San Francisco (at the heart of these ongoings) does make the temporary measure unwelcome at best.

The news also proves how the AI boom has created intertwined dependencies far deeper than data center hardware and software – physical security personnel also play a crucial role to the degree that instability poses potential risks to company offices.

And with both Anthropic and OpenAI workers recently facing threats, security remains a major focus.

• Flashpoint found ~34 insider threat posts daily on dark web between July 2025–26
• July 2026 saw 12,653 posts, with 75% from insiders selling access themselves
• Report warns insiders are now the weakest link, urging external monitoring of illicit forums

Every month, hundreds of people try to sell access to their employer’s IT infrastructure on the dark web. Some do it for the money. Others do it because they’re angry with their company for whatever reason.

As a result, malicious insiders are growing into one of the biggest, most dangerous threats for modern businesses, experts have warned.

This is according to cybersecurity professionals Flashpoint which published its latest monthly analysis of insider threat recruitment, illicit access advertising, and threat actor activity targeting enterprise environments.

Employees selling, hackers buying

As per the report, between July 2025 and July 2026, there were an average of 34 unique posts on the dark web, every day, which can be classified as “insider threat posts”.

That is roughly a thousand unique posts every month. In July this year alone, Flashpoint analysts identified a total of 12,653 insider posts, including both threat actors attempting to recruit insiders in target organizations, and insiders advertising their services. Of these communications, 1,132 were unique posts.

“As perimeter security, EDR coverage, and other security tools mature, threat actors are finding it faster—and cheaper—to target the human element and simply buy an insider’s credentials or pay an employee to open the front door,” Flashpoint said. “In a threat landscape where identity is becoming the primary attack surface, monitoring illicit marketplaces and recruitment efforts is critical.”

Perhaps the best example is the 2025 Coinbase attack, when hackers bribed overseas customer support employees to provide access to customer data. Coinbase said at the time that the insiders abused legitimate system access, causing a cyber-incident that ended up costing the company around $360 million.

Over the course of the year, the biggest targets were organizations in three industries: telecommunications, retail, and finance. However, July 2026 findings “noticeably deviate from this trend”, Flashpoint said, finding that more than half (58.6%) of all posts affect other industries.

The researchers were still hedging, though, saying that this could also just be a way for threat actors to find an alternative entry point into the target network. Preparations for a supply-chain attack, essentially.

This communication goes both ways, Flashpoint noted. Sometimes it is the criminals offering money for passwords/access, and sometimes it is the insiders advertising their services to the wider cybercriminal community. However, the scales are heavily tilted towards the latter. Just in July this year, more than three quarters (75%) of all unique threat actor posts came from insiders.

“This indicates a highly motivated internal threat landscape where disgruntled employees actively seek out buyers for corporate data and network entry points,” Flashpoint concluded.

Changing the behavior

This report can be both good news, and bad news, depending on the context. It means that software has gotten so good that cybercriminals are moving away from “cracking” it and towards targeting employees who are now the weakest link in the cybersecurity chain.

The bad news is that organizations need to rethink how they defend their perimeter and that they have quite a difficult task at hand:

“Insider threats are inherently difficult to detect using internal security controls alone because the malicious activity relies on valid credentials and legitimate access privileges,” Flashpoint explains. “Relying solely on internal logs means security teams often only detect an insider threat after data exfiltration or system sabotage has already occurred.”

Instead, organizations should monitor deep and dark web forums, invite-only threat communities, as well as encrypted chat platforms, to spot when someone is trying to buy or sell access to their IT infrastructure. They should also keep an eye on infostealer activity, compromised corporate credentials, as well as active session tokens, and make sure they are not used against them.

Finally, they should deploy third-party cybersecurity intelligence that equips teams with adversary TTPs.

Autonomous AI agents are moving faster than the frameworks meant to contain them. Enterprises are deploying agents that can call systems, pull data, and increasingly interact with other agents to complete multi-step tasks.

Yet few organizations can say with confidence exactly how many agents are running in their environment, what each one is authorized to touch, or who exactly is accountable when something goes wrong.

That gap can turn agentic workflows from a promising technological advancement into a potential minefield of risk unless enterprises take a new approach to governance – one that provides greater oversight into how agents are operating and what systems they can access, trust, and use.

Don’t wait for perfection

The market has responded to the potential threat around AI agents going rogue with a wave of new tooling: agent discovery platforms that scan for active agents, and agent harnesses that box them into approved boundaries. Both are useful, but neither solves the problem alone.

The challenge of “getting a handle” on AI agents is compounded by the pace of change. New agent tools, new AI model releases, and new orchestration options are arriving at a rate that makes any static governance model obsolete within months.

Rather than waiting for a “perfect” governance framework to emerge as a standard, organizations should take steps now to create a working structure that can evolve over time.

So, what might this look like in practice?

Continuous visibility and granular guardrails

Policies and procedures alone cannot confirm what is actually running in production. Organizations need a registration and discovery process that captures every agent in use, not just the ones teams report having built.

Real visibility comes from instrumenting the environment itself, using logging and observability data generated by the underlying models, and building analysis on top of it. Only that raw data can show what an agent is actually doing, how often, and at what cost, rather than relying on what people think it is doing.

Another key step is to move from broad guardrails to granular ones. A single, broad-based harness applied uniformly across an organization, for example, limits scope without addressing risk in a meaningful way. Different use cases call for different levels of restriction, and treating every agent identically either over-constrains valuable work or under-constrains risky work.

Guardrails need to be defined at the level of the individual agent and its specific task, not the organization as a whole. As agents begin accessing other agents to complete a task, that specificity becomes even more important: each agent needs an explicit, narrow definition of what it can do and what it can reach, so that any leakage beyond that defined scope is immediately visible.

With all of the above, enterprises should embrace an approach of “validate, don’t assume”. Putting a conceptual harness or policy in place is only the first step. Confirming that it actually holds under real conditions is a separate and ongoing exercise.

A useful approach here is to pair conceptual guardrails with a logical or physical enforcement layer: a tool that can confirm what is executable, what an agent can interact with, and whether it stays within that boundary in practice.

Continuous monitoring against policy, not a one-time sign-off, is what proves a harness is working, because agent capability and the surrounding set of tools change on a near-daily basis.

Access is not the only variable

Governance conversations tend to focus almost exclusively on what an agent can access. Equally important is the order in which it accesses information. An agent that gathers information out of sequence, or acts on data before a dependent step has completed, can produce results that are wrong even if every individual action was technically permitted.

Some workflows require steps to run in parallel; others require specific sequencing to ensure the most accurate results. Building a discrete, well-defined operational sequence for each use case, rather than allowing a model to determine its own order of operations, substantially reduces this type of error.

This is as much a process design issue as a technical one, and it relies on people defining the use case clearly before any tooling is applied.

A disciplined approach reins in agentic risk

None of these elements works in isolation. Discovery without granular guardrails leaves organizations knowing what exists but not controlling it. Harnesses without validation create a false sense of security. And governance focused only on access misses key variables that can introduce risk.

The organizations managing this well are the ones treating agent governance as an extension of existing IT and data governance, applying the same discipline used for foundational legacy systems rather than treating it as a bolt-on afterthought.

Given how quickly the technological landscape continues to shift, this comprehensive governed approach is what will keep agentic AI delivering value – and reduce the risk of agents going rogue.

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• SOCRadar uncovers AnonyMousKIT, a phishing kit abusing Apple’s Lost Mode contact info
• Crooks spoof Find My pages, trick victims into giving credentials to unlock stolen iPhones
• Active since 2024, it operates like a criminal software business with 500+ domains and AI‑driven vishing

Security researchers have discovered AnonyMousKIT, a new phishing kit designed to bypass the last line of defense for stolen iPhones, which has been in use for more than two years.

Apple devices come with several anti-theft mechanisms working together to make iPhones a lot less attractive to thieves, including Find My, Activation Lock, and Lost Mode.

If a user’s device is lost or stolen, they can use their tablet, laptop, or a similar device to enable Find My iPhone, which can then locate the device through an app or a website. They can also see its location on a map, make it play a sound, remotely wipe it, or receive a notification when it’s found. Enabling the Find My feature also turns on Activation Lock, which locks the phone and prevents it from being set up by someone else.

Even if the thief factory resets it, the phone remains connected to the real owner’s Apple account, and they simply can’t set it up. To do that, they would need the iPhone device passcode to exit Lost Mode, and the Apple account password, if Activation Lock/setup authentication is required.

But there is another feature Apple added, just in case the device isn’t actually stolen, but rather lost. For these occasions, there is an option to display the owner’s contact information on the screen so that a good samaritan who finds it can return it to its rightful owner.

As is the case with many other well-intended features, this one is now also being abused as part of the AnonyMousKIT phishing kit.

This is why we can't have nice things

According to security researchers SOCRadar, crooks are using AnonyMousKIT to create fake Find My or Apple pages. Then, they use the contact information displayed on the stolen iPhone to reach out to the victim. Through the kit, they can send emails, SMS messages, WhatsApp texts, or even AI-powered phone calls. Reaching out to the victim, the attackers introduce themselves as Apple customer support agents, and tell the victim their smartphone had been retrieved.

They also provide the victim with the correct model and IMEI details to confirm the authenticity of their claims. Then they require the victim to confirm their identity by visiting the spoofed Find My page and providing the credentials needed to unlock the phone.

The credentials end up with the attackers, who can then unlock the phone, wipe it, and sell it on the black market for a much higher price.

"Software business"

SOCRadar says the earliest records of the AnonyMousKIT date back to early 2024. Since then it has grown into a major operation, counting more than 500 domains, and having more than 150 storefront brands working as resellers and affiliates.

As part of their investigation, the researchers found records of roughly 200 calls, which the crooks made to victims between August 2025 and May 2026. The calls were done using five different AI agent personas and 55 different interaction transcripts.

Every call had cost the attackers $0.10, and most of them - 90% - were made to Brazilian victims. A small percentage of email correspondence was made towards government and corporate addresses, as well. Just under 30 attempts were made towards South African government domains, and three to a local university. While the campaign is global in its reach, it’s mostly focused on South Africa, Indonesia, India, Kenya, Brazil, and Italy.

SOCRadar describes AnonyMousKIT "not as a phishing kit but as a small software business with a criminal customer base."

"Its primary innovation is an automated, LLM-driven voice vector. At ~$0.10 per call, the platform initiates dynamic vishing across three languages using structured pretexts synced with email and SMS lure data, removing the need for fluent human callers."

At the moment the report was published, the campaign was still ongoing, and the researchers are still tracking it.

Via BleepingComputer

Artificial intelligence has transformed how software is built. Tasks that once took software developers days, if not weeks, to finalize can now be completed in hours with the assistance of generative AI tools.

The promise is compelling, offering faster innovation, increased productivity, and the ability to bring new applications to life at an unprecedented speed. But what is the impact on security?

AI coding has simultaneously put software risk on steroids. This is not because AI-generated code is uniquely flawed; it’s because it enables organizations to build and deploy software faster than any existing security, governance, or risk management process.

Development velocity has accelerated toward machine speed, while governance remains largely human-driven. That gap is now one of the defining software security challenges of the AI era.

Software is moving at machine speed. Security isn't.

AI is not only changing how code is written; it is changing how software is assembled. Developers can now assemble applications using open-source components, APIs, and third-party services faster than ever before. Every new application, integration, and dependency expands the attack surface that organizations must inventory, monitor, and secure.

The result is now a growing imbalance between software creation and software remediation. As the pace of software creation increases, remediation must keep up.

Veracode's 2026 State of Software Security report found 82% of organizations now carry security debt—vulnerabilities that remain unresolved over time — and 60% carry critical security debt, meaning flaws that are severe enough to cause significant damage if exploited.

Third-party code continues to be an especially stubborn source of risk, representing 66% of the most dangerous, long-lived vulnerabilities. The data reveals a simple reality: AI doesn't just generate more first-party code—it is increasing software complexity.

Organizations have always dealt with flawed code. The difference now is the speed and scale at which that code can be created, accepted, and deployed. AI doesn't just introduce risk, it amplifies the challenge of managing risk by enabling teams to generate exponentially more software than traditional security processes were designed to govern.

Traditional security governance assumes humans remain the bottleneck in software creation. Reviews, approvals, audits, and remediation workflows were designed for development cycles measured in weeks or months. AI-assisted development compresses those timelines dramatically.

When software can be generated, modified, and deployed at machine speed, governance models that depend on human intervention alone are no longer sustainable.

AI can help plant a seed, but that does not mean the garden will thrive. A seed needs the right soil, climate, and care. Software is no different. Organizations can generate applications overnight, but without the right security frameworks, operational support, and governance structures, those applications can quickly become liabilities rather than assets.

This is why security leaders must rethink governance for the AI era. The goal can’t be to inspect every line of code or eliminate every vulnerability before deployment; that approach was already becoming unsustainable before generative AI entered the picture. Instead, organizations need governance systems capable of operating at the same pace as software creation.

That means automating risk analysis, continuously evaluating dependencies, enforcing policies through pipelines, and prioritizing remediation based on business risk rather than relying on manual review alone.

Governance becomes the new trust layer

The need for machine-speed governance extends beyond operational efficiency. As AI accelerates software creation, governance becomes the mechanism through which organizations maintain visibility, demonstrate control, and establish trust across an increasingly complex software ecosystem.

Ultimately, this isn't just about scaling security. It's about ensuring software can be trusted and held accountable, regardless of how it's built.

AI can generate software, but it cannot assume responsibility for it. Boards will still hold executive leadership accountable for cyber risk. Regulators will still expect organizations to demonstrate that the software they deploy is secure and resilient. Customers will still expect software they can trust, regardless of how it was built.

AI may change how software is created, but it does not change who is accountable for its consequences.

That shift requires organizations to rethink governance as a strategic capability, not a compliance exercise. Success will depend less on preventing every vulnerability and more on demonstrating that software can be continuously evaluated, understood, and trusted as it evolves. In the AI era, the winners will not simply be those that build software fastest, but those that can govern it most effectively.

AI can help plant the seed, but it cannot tend to the garden. The organizations that lead today will not necessarily be those that generate the most software. They'll be the ones that can confidently answer the question every stakeholder will eventually ask: Can we trust what we've built?

AI has accelerated software creation beyond anything the industry has experienced before. If software risk is now on steroids, governance must be too. Otherwise, the gap between what organizations can build and what they can securely manage will continue to widen.

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Across organizations, AI adoption is entering a new phase. What began as experimentation and isolated use cases is rapidly evolving into enterprise-wide deployment, with AI becoming embedded across operations, customer experiences, decision-making and business strategy.

This shift is creating significant opportunities for growth, productivity and innovation, while also presenting a growing challenge for business and technology leaders: ensuring governance, oversight and operating models keep pace. As organizations scale AI, the question is no longer just what the technology can do, but whether the structures, processes and controls are in place to manage it effectively.

The growing governance gap

In many organizations, AI adoption is expanding beyond the direct oversight of central technology teams. Business units are deploying AI-powered tools to improve efficiency, streamline workflows and accelerate decision-making.

While this democratization of technology can unlock innovation, it can also create complexity. Leaders may find themselves accountable for outcomes generated by systems distributed across multiple teams, platforms and environments. The pace of adoption only intensifies this challenge.

Organizations are under pressure to move quickly as competitors invest in AI capabilities and employees increasingly expect access to AI-powered tools. The urgency is reflected in the UK government's AI Opportunities Action Plan, which highlights IMF estimates that AI could boost UK productivity by up to 1.5 percentage points annually, potentially generating £47 billion in economic gains each year.

As a result, deployment often progresses faster than governance frameworks can evolve.

This creates a fundamental tension between speed and control. Businesses want to capture the benefits of AI quickly, but moving too fast without appropriate safeguards can introduce operational, security and compliance risks. The challenge is not simply deploying AI at scale, but ensuring it can be managed responsibly once deployed.

As investment accelerates, expectations rise

As AI becomes more deeply integrated into business operations, its influence extends beyond execution. AI is increasingly shaping how work gets done, how decisions are made and how organizations allocate resources. In some cases, it is helping leaders identify opportunities and risks that may not have been visible through traditional approaches.

This growing influence means AI is no longer just a technology initiative. It has become an organizational capability that touches every part of the business. Decisions about AI deployment are therefore also decisions about governance, accountability and risk management.

The scale of momentum behind AI is clear. Earlier this year, the UK government highlighted £14 billion in private-sector AI investment commitments and more than 13,000 planned jobs as part of its ambition to establish the UK as a global leader in artificial intelligence.

This reflects a broader shift in how organizations view AI: no longer as an experimental technology, but as a strategic capability expected to drive growth, productivity and competitive advantage.

As investment accelerates, so too does the pressure to deliver measurable outcomes. Yet many leaders are discovering that success depends on more than deploying new AI tools. Without clear accountability, visibility and governance, the benefits of AI can be undermined by operational complexity, fragmented decision-making and increased risk.

The organizations that realize the greatest value from AI are likely to be those that invest as heavily in governance and oversight as they do in the technology itself.

Security, data and trust at scale

Security remains a critical consideration. As organizations integrate AI into business-critical processes, they must address issues such as data protection, model integrity and regulatory compliance. A single failure can have consequences that extend beyond technical disruption, affecting customer trust, brand reputation and regulatory standing.

Public expectations for responsible AI are high, with 72% of the British public saying that laws and regulation would make them more comfortable with the use of AI, underlining the importance of strong governance and oversight.

The challenge is heightened by AI's dependence on large volumes of data drawn from multiple environments and applications. Without visibility into how data flows through these systems, organizations may struggle to assess risk or respond effectively when issues arise. Robust governance and transparency therefore become essential components of any AI strategy.

Alongside security concerns, organizations are also facing greater financial scrutiny. Unlike traditional technology projects, AI programs often evolve rapidly, with new models, services and use cases introduced continuously. This can make it difficult to maintain oversight of spending, performance and risk, particularly as AI becomes embedded across multiple business functions.

These pressures are driving a reassessment of operating models. Traditional approaches to governance were largely built around systems that changed predictably and remained relatively static once deployed. AI introduces a different dynamic: models evolve, outputs vary and operating environments can change rapidly.

Building adaptable AI governance

As a result, organizations are increasingly recognizing the need to build adaptability into their AI strategies. Governance cannot be treated as a one-time exercise. It must become an ongoing capability supported by continuous monitoring, clear accountability and the ability to respond quickly to emerging risks and opportunities.

The organizations seeing the greatest success with AI typically view governance and innovation as complementary objectives rather than competing priorities. They focus not only on deployment, but also on visibility, control and resilience. By establishing clear frameworks from the outset, they create an environment where AI can scale responsibly and deliver sustainable business value.

Infrastructure strategy also plays a key role. Many organizations operate across multiple cloud environments and technology platforms, creating challenges around integration, portability and control. As AI workloads increase, flexibility becomes increasingly important.

Businesses need the ability to deploy, move and manage workloads efficiently without becoming constrained by fragmented architectures.

Scaling AI with confidence

Ultimately, the challenge facing leaders is not whether AI should scale, but how it scales. As adoption accelerates, organizations must look beyond deployment and focus on creating the conditions for sustainable success.

That means investing in governance, strengthening visibility across increasingly complex environments, improving financial accountability and ensuring organizational structures evolve alongside technological capabilities.

AI has the potential to transform how organizations operate, compete and create value. However, realizing that potential requires more than implementing new tools. It requires building the frameworks that allow innovation and control to coexist.

As AI becomes more deeply embedded across the enterprise, the organizations that achieve the greatest success will be those that can balance agility with accountability, enabling them to innovate confidently while maintaining oversight, resilience and trust.

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Although many people would think of phishing as a malicious email containing a suspicious link or attachment, hackers have now moved far beyond this to target trusted business tools including calendars and meeting invites.

We spoke to Soundharya Bharani Poomalai, Associate Threat Analyst, Barracuda, to find out more.

• Why are attackers increasingly turning to calendar invites and .ics files as phishing vehicles, and what has changed in the threat landscape to make this an attractive attack surface now?

Calendars have become part of daily business admin and are used far beyond meeting scheduling. People now get invites for things like policy acknowledgements, handbook reviews, benefits enrolment windows and compliance training reminders. A calendar invite referencing an HR update or a payroll action doesn't look out of place, and this allows attackers to blend in more easily than a suspicious email ever could.

There's also a structural advantage. Calendar entries get added automatically with little or no interaction from the recipient, and they tend to persist even if the original email is deleted or quarantined. Mobile adds another layer to this. A lot of calendar notifications get handled on phones, which often sit outside the reach of desktop-focused security tools.

• How significant is the visibility gap between what an email security system can inspect and what is rendered by a calendar application?

It is significant, and it’s something many organisations haven't accounted for. Traditional email security is built to scan the message body, subject line and attachments, whereas an .ics file often slips through as a calendar object and doesn’t receive the same level of inspection.

The problem is that .ics files carry much more than a date and time. They can include event descriptions, organiser details, locations, attachments, URLs and custom metadata fields, and any of these can be used to hide phishing content. Once the calendar app renders that content, the recipient sees corporate branding, instructions or a QR code that looks entirely legitimate. If the victim then enters their credentials and completes MFA, the attackers can intercept the username, password and session data, giving them full access to the account.

That mismatch between what security tools check and what the user sees is why these attacks succeed.

• Are calendar phishing attacks fundamentally a new technique, or are they an evolution of the same social-engineering and evasion techniques we've seen in email phishing – such as QR codes, impersonation and adversary-in-the-middle attacks?

They’re an evolution rather than an invention. The building blocks are familiar. QR codes hide a destination, brand impersonation builds trust, and adversary-in-the-middle platforms intercept credentials and session data in real time once someone signs in. None of this is new.

What's changed is the delivery container as they’re wrapped inside a calendar invite instead of an email. A QR code buried in an .ics file benefits from all the same advantages it has in a PDF or email body because it avoids text-based detection. It also gets the added benefit of sitting somewhere security tools are paying less attention.

• What does the rise of .ics phishing tell us about how organisations need to redefine what constitutes ‘malicious content’?

It tells us that malicious content is no longer confined to a bad link or attachment. Security teams have traditionally focused on email bodies and file attachments because that's historically where the risk sat. Calendar invites increasingly need to be factored into this as a phishing vehicle. The format of the content is not what determines the risk now, it’s what the content does when it reaches the recipient.

All of this means organisations need to start thinking about any workflow that can display or trigger content on a user's behalf.

What should organisations be inspecting inside a calendar file, and what are the technical challenges involved in doing that effectively at scale?

Calendar files need the same level of scrutiny as a traditional attachment. In practice, this involves parsing the metadata fields, analysing any embedded links or attachments, inspecting HTML-rendered content, and decoding QR codes to check where they lead to.

The technical security challenge is that .ics files were built for interoperability. The format allows an event title, description or organiser field to carry rich content across Outlook, Google Calendar and Apple Calendar, and that flexibility is what makes it hard to inspect consistently at scale.

There's also the matter of what happens after delivery. A calendar entry can sit in someone's diary for days or weeks before a link becomes relevant, so inspection can't just happen once at the point of delivery. It needs to hold up over time.

If a malicious calendar invite gets through, what should the incident-response process look like? Is deleting or quarantining the original email enough?

Deleting or quarantining the original email doesn't remove the calendar entry itself. Because invites are typically added automatically, the event can remain live in someone's calendar even after the source email is long gone, which means the malicious link or QR code is still sitting there and waiting to be clicked on.

A response will only be effective if it removes both the delivery message and the associated calendar entry from every affected mailbox. Teams also need to check identity activity around the time the invite landed and look for things like sign-ins from unfamiliar devices, unexpected MFA prompts, new session creation or OAuth consent activity. If someone did interact with the invite, credentials or session tokens may already be compromised, so containment can't stop at cleaning up the calendar.

What are the three most practical things security and business teams can do today to reduce their exposure to calendar-based phishing without disrupting legitimate use of calendars?

First, treat calendar invites as active content rather than passive scheduling data. Apply the same inspection standards to .ics files that already exist for attachments, including checking embedded links, attachments and any QR codes inside the event itself.

Second, strengthen identity security so a successful phishing attempt doesn't automatically become a successful breach. Phishing-resistant MFA such as FIDO2 or WebAuthn, conditional access policies, and the ability to monitor sessions and revoke them quickly all reduce the impact if someone does click through.

Third, update user awareness so people know calendar invites can be malicious too. Employees should be wary of QR codes inside calendar events and treat unexpected HR, payroll or policy notifications arriving as .ics files with the same suspicion as an unusual email.

Are we reaching a point where organisations need to stop thinking of email as the attack surface, and instead think about all the trusted applications and automated workflows that email can trigger?

Yes, and calendar phishing is a good example of why. An email triggers a calendar entry, a calendar entry contains content, and that content leads somewhere else entirely, whether that's a fake sign-in page or a malicious QR code.

Every one of those steps happens in a different application, often with a different set of security controls, or none at all. Attackers are simply following that chain to find the weakest link, and the weak link tends to sit wherever inspection stops.

Organisations that only defend the inbox are defending one part of a much longer chain.

• GTA 6 leak hunters now face fake downloads designed to compromise their computers
• The suspicious ISO reportedly disables Windows security tools after execution on affected systems
• A tiny 50KB payload allegedly sits inside a gigantic 113GB file

Keen Grand Theft Auto 6 fans searching for leaked copies now face another risk: a huge ISO file is reportedly circulating across torrent sites.

The file is said to measure 113GB, yet online testers claim almost all that space consists of empty data rather than genuine game assets.

Their analysis reportedly found a small malicious program hidden inside the file, making the download appear far more dangerous than it normally would.

The huge size made it feel genuine

The suspicious file reportedly began circulating on torrent sites after renewed interest in leaked GTA 6material and claims of a complete game build.

However, testers who examined the ISO reportedly found that it contains 99.99% empty zeroes, leaving only about 50KB of data identified as malicious code.

“I did some reverse engineering and confirmed that it is fully fake and full of viruses,” said @Aidas29506493, an online researcher on X.

The 113GB figure was created to make the file resemble a legitimate game release while concealing a much smaller payload, which could be ransomware.

Yet file size alone provides no evidence that an alleged copy contains authentic game assets or executable code from Rockstar.

The alleged code reportedly includes commands designed to weaken Windows Defender and interfere with other security tools on affected computers.

Such behaviour would allow the malware to operate with fewer protections after someone launches the downloaded file on Windows.

The analysis also reportedly found a PowerShell command that adds the system drive to Windows Defender's exclusion list for scanning.

Another command was said to terminate security software, although independent verification of those findings remains limited at this time.

Malware risk rises alongside GTA 6 leak interest

The alleged ISO follows a series of GTA 6 leak claims that have generated substantial interest across gaming communities online.

A leaker known as Cyberleek has reportedly shared gameplay material and the game's fictional Leonidas map, while demanding changes from Rockstar.

Take-Two Interactive has also sought information from Microsoft that could help identify users connected with three Discord servers reportedly linked to leaks.

Those developments have increased attention around unofficial GTA 6 files, creating conditions that criminals can exploit with convincing fake downloads.

This situation also shows why unreleased game files can carry greater security risks than ordinary pirated software already available publicly.

Gamers downloading such files may expose passwords, personal files, browser data, or other information if malicious code gains access without warning.

Rockstar has scheduled the official release of Grand Theft Auto 6 for November 2026, giving players a legitimate alternative to unofficial copies elsewhere.

Until then, claims surrounding leaked builds and supposed ISOs should be treated cautiously because the files cannot be independently verified as genuine.

This case provides a strong warning, but further independent testing would be needed to establish every technical claim about the file itself.

Via Toms Hardware

• Researcher Dominik Reichel found SLEEPWALKER, a silent malware implant disguised as ESET’s agent
• It contains no malicious code, activates only after receiving crafted network signals
• Likely a nation‑state project targeting specific victims; no active campaigns confirmed yet

Security researchers discovered a new and rather unusual piece of malware.

Most malware come with a built-in, pre-defined set of tools and features: system fingerprinting, network mapping, data exfiltration, keylogging, screenshots, tapping into the camera and microphone. When they infect a machine, they first try to phone home using the device’s internet connection and await instructions on which of the features to use.

But security researcher Dominik Reichel found something entirely different: a piece of malware not having any of the above, designed to remain almost completely silent until being “woken up”. He named it SLEEPWALKER.

No active campaigns

This implant has no malicious code, and therefore nothing that would get flagged by security software. It hides in plain sight, masquerading as a legitimate Windows component for ESET’s Management Agent. This allows it to run from within a trusted app, instead of being a standalone program that could invite scrutiny.

SLEEPWALKER listens to network traffic for a specially crafted signal, waking up only when it is received. That signal also “teaches” the malware what it can do - schedule different activities, communicate with other systems, receive additional programs, and even execute code.

The malware was submitted to VirusTotal sometime last year, Reichel said. It was not found in any active campaigns, and there are no confirmed victims, industries, countries, or organizations associated with the sample. Reichel also stressed that it’s unknown how the malware initially entered the reporter’s environment, who runs it, and what additional tools may have accompanied it.

He also said that the code is somewhat “rough around the edges”. Despite its unusual design, it comes with several weaknesses, which might suggest that SLEEPWALKER was a work in progress. He doesn’t know if there are newer variants in the wild, though.

Still, given the nature of the malware, Reichel doesn’t think it was built for indiscriminate attacks. Instead, it was most likely designed by nation-states with specific targets in mind.

Via The Register

• Kaspersky found Android malware abusing DoFun car head units via TWCore updates
• Multi‑stage attack installs loaders and reverse proxy, aiming to build a botnet of connected cars
• Campaign attributed to MoYu Group; DoFun patched vulnerabilities after disclosure

We’ve seen botnets comprising cameras and DVRs, we’ve even seen botnets comprising smart fridges and digital frames, but we’ve never seen botnets comprising automobile infotainment systems. First time for everything.

Earlier this week, security researchers Kaspersky warned about finding a brand new Android malware targeting the car’s head unit. The victim seems to be a Chinese manufacturer called DoFun. Head units from this manufacturer, built on Android, are running an app for analytics and software updates called TWCore.

According to Kaspersky, the attackers abused TWCore’s update mechanisms, instructing it to download a malicious APK. This malware is then placed in the app’s cache directory and installed by the legitimate com.tw.core package.

No active campaigns

The researchers said this was a multi-stage attack. In the first stage, a tiny dropper with no user interface gets deployed. It decrypts embedded data, and extracts the information it needs for stage two. In the next stage, the loader contacts the attackers’ server and gets instructions about stage 3, which can be different things, from deploying additional malware, to running the “zhima” reverse proxy.

Despite its multifunctional nature, Kaspersky believes that the true goal of the campaign is to assimilate the cars into a botnet. Some cars come with a SIM slot and are connected to the internet 24/7. It is probably not an exaggeration to say that cars just might be the perfect devices for a malicious botnet.

Kaspersky attributed the campaign to MoYu Group, a threat actor known for building malicious botnets based on Android devices. In the past, this group was observed building the BadBox botnet out of Android smartphones, tablets, streaming devices, and other internet-connected hardware.

The researchers notified DoFun of their findings, and the vulnerability was quickly fixed: "We notified the vendor about the distribution scheme, and they subsequently reported fixing the security issues," the researchers said.

Via The Record

• Crooks used Google Sites and stolen Google Ads accounts to push fake OpenAI Codex pages
• macOS users tricked into pasting Terminal commands, leading to AMOS infostealer infection
• Campaign abuses Google’s trust signals; Windows download button was a decoy, only Mac payload worked

Cybercriminals were seen abusing Google Sites, the Google ad network, and OpenAI’s good name, in a campaign that targets macOS users with infostealers.

According to security researchers CATO CTRL, the crooks used Google Sites to create a fake version of the OpenAI Codex download site. To avoid being flagged by Google’s security systems and ultimately removed, the site itself contains no malicious code or download links, whatsoever. Instead, it hosts an iFrame that displays content hosted elsewhere.

Then, they advertised that site on the Google Ads network. Google is usually good at spotting and preventing malicious ads from running on its network, but sometimes threat actors steal legitimate accounts with good standing and use them to bypass automated scans and get the ads listed, while also spending other people’s money on the ad campaign.

Not ClickFix

The ads were displayed to users searching for “codex macos download”, at the very top of the page. Using both Google Sites and Google Ads is a deliberate attempt to appear legitimate and trustworthy since after all, many people trust whatever Google displays as the top result without double-checking or scrutinizing the result.

Those that do click will see a website that, by all accounts, looks like OpenAI’s download site for Codex, the company’s AI coding agent. The site has download buttons for both Windows and Mac, but only the latter works. The download and installation process was designed to look “advanced” - instead of getting an executable, the victims are told to paste a command in Terminal.

Cato’s researchers call this a ClickFix attack, but ClickFix usually displays a fake problem, before offering an equally fake solution. This looks more like another way to appear legitimate because after all, several AI agents are specifically designed to be installed and run from the macOS Terminal, including OpenAI’s Codex CLI.

The end goal of the campaign is to deploy AMOS, a known macOS infostealer capable of grabbing browser data, login credentials, cryptocurrency wallet information, and more.

Via SiliconANGLE

Most organizations in UK and across Europe don’t struggle to recover from cyberattacks such as ransomware because they lack backups. They struggle because they try to restore everything at once.

In the aftermath of a major cyber incident, the instinct is to bring every system back online as quickly as possible. It feels like the fastest path back to normality. However, in reality, this approach often slows recovery down, reintroduces cyber risk and undermines trust just when the organization needs it most.

In fact, those that recover fastest start from a different premise. They assume large parts of their organization will be unavailable or untrusted, and they plan accordingly. This mindset leads to a much clearer goal - restore what matters most, quickly, and in a state you can trust.

What is critical to survival?

Focusing on the critical areas of an organization is the idea behind the Minimum Viable Company (MVC) concept, sometimes referred to as the Minimum Viable Organization. It is a definition of what must exist for the organization to survive in challenging conditions such as a cyber incident.

It’s not just a technology concept, it’s a business definition of survival in terms of the minimum combination of people, processes, technology, documentation, facilities, and third-party dependencies required to keep a business functioning and creating value.

Where to start getting an MVC up and running?

There are five key capabilities when it comes to operationalizing an MVC:

1. Clarity on critical services:

A precise understanding of the systems and dependencies that directly support revenue and mission-critical operations is needed here. To understand the MVC, it is key to map systems to business value. Without this understanding, it’s impossible to accurately define the MVC.

The first steps focus on undertaking a structured assessment, aligning across business and technology stakeholders, and going through a realistic simulation of how recovery will unfold under pressure. This will uncover the key areas needed to provide just enough capability to keep the organization functioning safely during a crisis and guide recovery.

In practice, this means defining what must function in the first 24 hours, the first 72 hours, and the first week after a disruption.

2. A trusted foundation (Tier 0):

In the event of a cyberattack, many organizations miss the critical foundational layer that allows them to establish identity and access control independently of compromised systems.

This foundational layer is what we call Tier 0 or the control plane for recovery. It includes identity and access management, networking and DNS, privileged access controls, core security tooling, physical access systems, and secure communication channels.

It also covers non-technical dependencies that are easy to overlook until they’re urgently needed such as incident response playbooks, contact lists and escalation paths, insurance policies, and contracts with external responders. These are the foundations underpinning the critical systems that need to be restored after a cyber incident. Without this layer, a trusted recovery is not possible.

3. Isolation of recovery assets:

In the event of a cybersecurity breach, organizations must establish control of their most critical systems. This requires recovering data separately from clean snapshots and investigating in parallel, not sequentially, to ensure the recovered systems are not infected by malicious software.

As part of this process, backups, configurations, and recovery tooling must be protected from the same blast radius as production. If key recovery assets can’t be isolated, a rapid and trusted control of critical systems can’t be achieved.

4. Clean-room recovery capability:

To set up an isolated environment to rebuild systems without reintroducing compromise, organizations need to set up what we call a ‘Digital Jump Bag’. This is a secure, isolated repository containing everything required to establish a trusted recovery starting point to rebuild systems without reintroducing compromise.

5. Validated ability to operate

The next step is to validate the ability of the MVC to operate through realistic crisis scenarios. Resilience must be proven under real-world conditions. Practice is important here because an untested plan remains theoretical. Rehearsals will also help to answer the Board’s most direct question in the event of a cyberattack - how long will it take to restore critical services to a trusted state?

Recovering faster by restoring what matters most

The most common cyber resilience risks are failing to define what must come back first and how to bring it back in a state that can be trusted. That’s the difference between recovery as a process and recovery as a capability.

The MVC isn’t static. As an organization evolves, its definition must evolve too. But the principle stays the same: recovery improves when organizations stop trying to restore everything and start restoring what matters.

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Cyberattacks are increasing in speed and sophistication, ranking among the biggest threats to businesses of all sizes and industries.

Previously unprecedented costs have made headlines; in October, a report from the Cyber Monitoring Centre revealed that the cybersecurity incident which affected Jaguar Land Rover in August cost the UK economy an estimated sum of 1.9 billion pounds.

Meanwhile, in North America, the aerospace sector has seen a spate of attacks, with WestJet’s June cyberattack resulting in the theft of 1.2 million passengers’ data while the data sets of 1.5 million flyers are believed to be compromised following September’s attack on Collins Aerospace.

Research released earlier this year revealed a stark dip in public trust; when questioned on which industry consumers trusted with their data, no single sector saw an approval rate of above 50%.

Without a clear and modernized security strategy, businesses are leaving themselves vulnerable to the far-reaching consequences of a breach, including reputational damage, operational delays, and financial loss.

Why Zero Trust Matters

AI has given everyone with a computer, tablet, or even smartphone easy access to automation, including those using it with malicious intent. In the cyber space, this allows criminals to continually change their approach, scaling their efforts and exploiting vulnerabilities in their target’s software. To keep up with evolving threats, Zero Trust is vital, prioritizing data over assumptions is not optional.

The contemporary approach to the principle of Zero Trust has been developed from the work of the Jericho Forum, made up of industry experts keen to establish a ‘de-parameterized’ model that enables a more granular and flexible approach to security.

The group, which later became part of The Open Group Security Forum, paved the way for John Kindervag’s popularization of the Zero Trust principle in 2009, emphasizing the importance of 'Never trust, always verify'. In a conversation with Gartner’s Neil MacDonald, he further explained that “Zero Trust is not a technology; it’s a security philosophy that rewires how we think about access”.

It's a smart idea; allowing security systems to keep up with industry change. In reality, however, despite the majority (96%) of companies incorporating, or planning to incorporate, a Zero Trust strategy, only 35% have made it to the implementation stage.

To decrease the frequency of corporate security breaches, this needs to change. The key to success? A mutually agreed understanding of what Zero Trust is and a cross-industry implementation drive.

Steps to a Modernized Security Strategy

The traditional business approach to cybersecurity, including an over-reliance on VPNs, led companies to draw a single ‘perimeter’ around their data. Thus, once breached, cybercriminals were able to steal and duplicate data from across the organization. In contrast, with a strategy led by strict adherence to the Zero Trust principle, access is confined to the singular section where the incident occurred.

The successful implementation of the Zero Trust principle requires a focus on data and information security across all networks and platforms. For example, the evaluation of risk should take place on a case-by-case basis, with deliberate decisions made to accept, mitigate, or transfer. With this approach, security teams have the flexibility needed to safeguard data and, when inevitable breaches do occur, ensure hackers can only access the top layer of information.

The use of Zero Trust as a basis for risk management necessitates a security infrastructure that does not become stagnant but is ever-changing to prevent cyberthreats from impacting the organization. At any point, a key tech stack component can become a target for criminals. In response, to drive resilience, security professionals should track any attempts, and techniques used, amending infrastructure in tandem.

Across sectors, cybersecurity budgets are growing. In March, the IDC's Worldwide Security Spending Guide predicted a spending growth of 12.2% in the year that followed, growing to $377 billion by 2028. Though this reflection of increased enthusiasm to strengthen security infrastructure is a step in the right direction, organizations need to ensure this investment isn’t just a one-off. Every element, and everyone’s access, must be continually questioned to protect from the escalating threat posed by a breach.

Designing a Forward-Looking Security Strategy

Solutions adopted with cybersecurity in mind may differ between organizations but, to drive resilience in the long-term and successfully embrace the principle of Zero Trust, vendor-neutral definitions of methodology and standards will be a necessity.

Once these are widely acknowledged, a commitment should be made to strengthening the underlying security infrastructure over the long-term, driven primarily by the principle of Zero Trust. In a landscape where security threats are ever-evolving, so too should each corporation’s ability to protect themselves from malicious actors. This includes blocking access to valuable data sets in the event of a breach and ensuring each person responsible for security is kept up to date with the latest insight on the nature of threats.

When applied in practice, though the exact methods and vision may differ, the guiding principles should remain consistent. To establish necessary considerations, organizations can start with the Zero Trust Commandments, which include the need for security to be integrated through culture and processes, the implementation of asset-centric controls, and the explicit validation of trust through using all relevant information available.

A news landscape dominated by security breaches and cyberattacks has created a generation of consumers that, more wary than before, are no longer shocked when the personal information they have willingly shared with corporations is taken with malicious intent. It should be expected that they will think twice before trusting a new organization with their information, placing an onus on businesses to prove that their infrastructure is robust.

As we look ahead to 2026, the companies that succeed will prioritize security, consider wider society expectations, and prove their commitment to Zero Trust in each decision they make.

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• Hackers exploited trusted software updates to deliver malware directly into car head units
• Kaspersky says this is the first campaign tailored specifically for vehicle head units
• The malware can run silently without showing drivers any visible interface

Car head units are now being drawn into a growing wave of Android malware campaigns built for connected vehicle systems, experts have warned.

A newly discovered malware campaign is infecting these head units directly, systems that combine multimedia functions with, in some models, vehicle control.

According to Kaspersky, this campaign marks the first documented case of malware built specifically for this type of infection chain.

Compromised update channels deliver malware straight into vehicles

Researchers believe the activity can likely be traced back to the MoYu Group, a threat actor closely tied to the well-known BadBox botnet, which spread through the legitimate update mechanisms built directly into the firmware of Android-based head units manufactured by DoFun.

The infection chain originates from TWCore, a legitimate system app that is normally responsible for collecting analytics and updating head unit software remotely.

Attackers hijacked this trusted update channel using a specialized dropper called JarService to deliver previously unknown malware directly onto a range of affected devices.

Once successfully installed, the malware operated quietly as a regular background application without ever displaying any visible user interface.

Kaspersky identified nine distinct remote commands built into the malware, capable of displaying unwanted ads and executing various forms of ad fraud.

The malware also actively collected sensitive device information, including display resolution, device model, Wi-Fi network identifier, and the device's MAC address.

Investigators found clear technical links between this campaign and prior attacks launched against TV set-top boxes tied to the same broader threat group.

The research team claims that the botnet's administration panel shares embedded URLs with residential proxy service websites PXYEDGE and ProxyForU.

BadBox itself operates as a large, sprawling network of hijacked Android devices, including streaming boxes, phones, and tablets that arrive pre-infected from the factory.

Kaspersky has already formally notified the vendor about this ongoing abuse of its legitimate software distribution channel and update infrastructure.

According to statements from DoFun, the underlying issue has since been resolved across most affected devices currently deployed in the field.

Head units present a growing and largely unprotected attack surface

Car head units can arrive factory-installed directly from the manufacturer or get added later to older vehicles as aftermarket upgrades.

Manufacturers frequently rely heavily on the Android operating system because it simplifies interface customization and essential system integration work considerably.

This widespread industry reliance means most standard Android applications, along with most existing Android malware, can potentially run on these devices.

Head units rarely store sensitive personal data directly on board, which on the surface might suggest only limited appeal to attackers.

However, they typically include active SIM card slots and maintain constant internet connectivity for navigation services and routine software updates.

That particular combination of persistent connectivity and comparatively weak security oversight makes these systems a genuinely attractive prospect for attackers going forward.

The overall scale of this particular campaign remains genuinely unclear, and whether other head unit manufacturers face similar exposure is not yet known.

• Expel researchers warn of SynkLoader backdoor spread via fake IT help desk Teams messages
• Malware modules include PhishLocker (fake login screen harvesting OS passwords) and Interactive Shell for remote control
• Defenses: distrust unsolicited Teams DMs, verify with IT before installing apps, and train staff against social engineering

For roughly a month now, cybercriminals have been targeting organizations with a new backdoor malware called SynkLoader.

According to security researchers Expel, the attack starts with social engineering. Victims would get a Microsoft Teams message from a person claiming to be from the company’s IT help desk. They would tell the victim their computer is having an issue, and that they need to install a “PowerShell Cleaner”. This fake program is nothing more than a malicious framework, hosted on Microsoft Azure to increase its trustworthiness.

The malware itself comes with a number of different modules, giving the attacker a range of features, from harvesting system information, to creating a reverse proxy. Two particularly worrying modules are called PhishLocker and Interactive Shell. The former creates a convincing, yet fake, Windows lock screen, which can harvest the user’s OS login password.

This is not SickKids' first attack

BleepingComputer argues that with this password the attackers could “access corporate environments from the infected device, bypassing IP allow-list restrictions”. Those with a sharper eye might spot the ruse, as a simple Alt + Tab shows that the login screen is nothing more than a “full-screen borderless GUI application”.

The other module - Interactive Shell, allows threat actors to remotely execute PowerShell commands and receive the output, which essentially grants them full control over the infected device.

The full list of Indicators of Compromise (IoC) can be found on this link. To defend against these types of attacks, target companies should instruct their employees not to trust unsolicited Teams messages at face value, and not to install any applications without double-checking (calling) with their IT department first.

Alongside phone calls, Microsoft Teams is one of the most-used channels for initial contact and compromise. Also, employees remain the weakest link in every company’s cybersecurity chain, unwillingly granting attackers access or sharing login credentials.

Via BleepingComputer

• SickKids hospital in Canada hit by third‑party software vulnerability, exposing employee data
• Clinical systems and patient records unaffected; patient care continued without disruption
• Affected staff and applicants offered 24 months of free credit monitoring and identity protection

The Hospital for Sick Children, a major pediatric hospital in Canada, suffered a cyberattack that affected parts of its website, and resulted in the loss of some employee personal information.

In an announcement published on its website, the organization (also known as SickKids) said the unnamed attackers abused a “vulnerability in a third-party software application used by SickKids and other organizations.” The announcement did not say exactly which app was used in the attack, or what the vulnerability was, but stressed that clinical systems and patient information were not affected.

“Patient care has continued as usual”, it added.

This is not SickKids' first attack

After launching an investigation, SickKids learned that personal information of some former and current employees working at SickKids, Boomerang, and SickKids Foundation, as well as SickKids job applications, was exposed. It did not detail the nature of the exposed information, or how many people are affected.

Whatever that number is, those people have been offered 24 months of complimentary credit monitoring and identity protection services, for free.

“We remain committed to maintaining strong protections and continuously enhancing our cybersecurity measures to help protect the information entrusted to us,” the company concluded. Ironically, SickKids was also committed in late 2022 and early 2023, when it was struck by LockBit and had its systems locked down by the ransomware threat actor.

While, in that incident, LockBit apologized, gave the decryptor away for free, excommunicated the affiliate responsible, and did not mention any stolen data, by late 2022 double extortion attacks were standard practice, meaning data was likely exfiltrated then, as well.

At the time, LockBit was one of the most active and most dangerous ransomware operators. In early 2024, its operations were severely disrupted through Operation Cronos, but it seems the group is making a comeback. There are reports from late 2025 of LockBit 5.0 claims, including a purported attack on U.S Bank, but the news is yet to be confirmed.

Via The Record

• Apollo confirms July 2026 cyberattack via social engineering exposed PII in its cloud environment
• Data included names, DOB, contact info, addresses, and Social Security numbers
• Firm offers two years of identity protection; no evidence of dark web leaks yet

Apollo, one of the biggest private equity firms in the world, has confirmed it suffered a cyberattack which compromised some people’s personally identifiable information.

The company notified California’s Attorney General’s Office about the breach and shared a copy of the letter it is now sending out to affected individuals. It is impossible to discern from the letter if the victims are Apollo employees, customers, or someone else entirely, but the company did clearly explain what happened.

As per the letter, an unidentified threat actor tricked an Apollo employee into granting them access to the company’s cloud environment. The attackers used social engineering (usually phishing), which means the victim either tried logging in using a spoofed landing page, unknowingly installed an infostealer, or was convinced to grant the attackers access via remote monitoring and management software.

Was there really a hack?

The company spotted the attack a few days later, and after activating its safety protocols (notifying the police, enhancing its security protocols, and bringing in third-party forensic experts), launched an investigation which showed that the attackers accessed its cloud platform between July 6 and 10.

“During our investigation, we learned on August 12, 2026 that the information potentially impacted by this incident included your name, date of birth, contact information, home address, and your Social Security Number (SSN),” the company said. This means that financial data such as credit card or bank account information, was not compromised.

Still, cybercriminals can make use of this type of information, as is often the case in identity theft, business email compromise, and even wire fraud.

Apollo is now offering two years of free identity theft protection and monitoring for affected individuals through Cyberscout.

At press time, no threat actors claimed responsibility for the attack, and the data has not yet surfaced anywhere on the dark web.

Via TechCrunch

At 2 a.m., an automated remediation agent detects a problem on the network, traces it to a misconfigured policy, validates through the harness that the proposed fix operates within the given policy boundaries and fixes it. The network stabilizes. Nobody’s notified.

In the morning, a human reviews the agent's daily insights: a summary of all the changes executed, with links to the logs, audit trails, reasoning and root cause behind them, confirms everything has been properly resolved and moves on. That is what Human-on-the-Loop looks like.

At another organization, at 4 a.m., a DIY-built, vibe-coded remediation agent detects a problem on the network, traces it to a misconfigured policy, and fixes it. The network stabilizes. Nobody’s notified. In the morning, a human reviews the logs, assumes the issue has been resolved, and moves on.

Where's the difference?

The difference is that, in the DIY scenario, the logs only tell part of the story. They don't show that the agent made three other changes to get there, which were broader than intended, and the decisions behind those changes weren’t flagged because nothing in its constraints required them to be.

This is what the move toward Human-on-the-Loop can look like without the right controls in place. No dramatic handover. Just a series of small, reasonable delegations that gradually build into something nobody explicitly signed off on.

And it's happening faster than most leaders realize. According to recent research, 57% of IT leaders expect to remove humans from the loop within a year or less, and 79% already treat AI agents as "users" who require their own identity management and governance controls.

The shift to agentic AI is happening faster than most organizations are prepared for, both in terms of governance and the ability to evaluate autonomous systems.

The illusion of "I approve"

The answer to autonomous AI has long been quite simple: keep a human in the loop. Somebody who reviews the output, hits approve, preserving accountability. Except it isn't, not really. Reviewing every action doesn't automatically create accountability, and it also prevents organizations from realizing the full benefits of autonomy.

Rather than reviewing every individual action, humans should be focused on evaluating outcomes, ensuring the system operated within its intended boundaries, and providing feedback that improves its performance over time.

Approval can become a ritual without meaning. As systems prove reliable and the number of alerts multiply, humans sometimes start to treat intervention as something that isn’t often needed.

The approval can become more of a click than a considered choice. And when something goes wrong (for example, a misconfigured policy, an automated remediation that turns into an outage), the question of who was responsible is difficult to answer.

It also reinforces a broader shift: one of the most important human capabilities becomes critical thinking and the validation of hypotheses, rather than the execution of tasks.

And something else is happening. Humans are transitioning from doing to reading before approving – a fundamental change in the day-to-day work of most of us.

Also, attribution isn't the same as provenance. A log that records what an agent did tells you almost nothing about why, or what shaped that decision. When things go wrong, those are the things you need to know.

Non-human identities and the new network population

When AI agents act on your network, querying systems or making configuration changes or routing traffic, they are effectively users. They need credentials, policies, guardrails, explainability, and audit trails just like human operators.

Most organizations haven't caught up with this. Identity frameworks were built for people, and applying AI agents to them as a kind of afterthought creates exactly the sort of shadow access that security teams spend their lives trying to eliminate.

The near-80% of leaders who already treat agents as governed identities are ahead of the curve. The rest are collecting risk they can't quantify, until it crystallizes into an incident.

To get this right you have to treat each agent as a principal with bounded permissions, time-limited access and a clear revocation path, along with a complete record; not just of what it did, but of what it was allowed to do and why.

Autonomy isn't given, it's earned

Network and security teams already know how to do this. Every enterprise has access control frameworks that govern what humans can reach and when. A junior engineer doesn't walk in on their first day with total production access. They gradually earn it, and this same logic needs to apply to AI agents.

We're not quite there yet. Too often, autonomy is treated as all-or-nothing. That isn’t the right model. Trust when it comes to humans doesn’t work like that, and it shouldn’t with AI either. Start agents in suggestion mode and let them prove themselves within clearly defined limits before expanding what they can do. And make sure every decision leaves a trail that explains not just what happened but the reasoning behind it.

As the use of autonomous AI grows, organizations will need to balance human oversight with systemic governance. People remain responsible for reviewing not only the outcomes AI produces, but, where necessary, the actions it takes and the reasoning behind them.

However, as the volume and complexity of autonomous decisions increase, this oversight should be complemented by infrastructure that embeds identity, policy enforcement, observability and accountability. Together, these controls help ensure AI actions remain transparent, traceable and aligned with organizational intent.

Building multi-agent systems that hold up under pressure

The more capable these systems become, the more organizations will move toward multi-agent architectures. This is where specialized agents each own a piece of a workflow and hand off context as they go. That's where things get complicated.

A single agent misbehaving is traceable. A chain of agents, each acting on the outputs of the last, is much harder to untangle when something goes wrong unless the right architecture and governance are in place. You need to know what each agent knew, not just what it did.

Do the boring part

Two organisations deploy the same AI-powered networking agent. One of them has done the unglamorous work: setting up tight permissions, proper identity controls and audit trails that actually answer questions. The other one hasn't.

You won't know the difference until something breaks.

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There’s a pattern starting to emerge with AI.

At first glance, everything looks like progress. AI is being adopted quickly, embedded into products, talked about in boardrooms, and pushed into real-world use faster than anything we’ve seen before. But as businesses become more accustomed to AI and increasingly find new ways to use it, there is a greater problem brewing that has the potential to be detrimental to a company’s cybersecurity posture.

Organizations are moving quickly to use AI, but far fewer are making the right decisions about how it’s actually being delivered and secured. And the gap between those two things is widening, with security teams left scrambling to fix vulnerabilities like whack-a-mole.

The speed is understandable. AI hasn’t followed the usual enterprise lifecycle. It hasn’t patiently moved from concept to pilot to controlled rollout. In many cases, it’s gone straight from experimentation into something business-critical, stitched together from APIs, models, agents, and data sources that weren’t originally designed to work together in this way.

That creates something fundamentally different. Not just another application, but something more fluid, a tool that behaves dynamically to make decisions and interact across multiple layers of the stack in real time.

And this is where the problem begins.

Where AI security currently breaks down

While the architecture that needs to be secure has changed, the thinking around security largely hasn’t, meaning traditional security measures are still being applied to situations they aren’t built for. Most organizations believe they have this covered. They’ve extended their existing controls, added new tools and invested in visibility. On paper, it looks like a sensible evolution of what they already had that keeps up with AI.

But in reality, much of that security still sits around AI rather than within it.

These traditional methods are protecting edges, monitoring outcomes and analyzing behavior after the fact. What they’re not consistently doing is sitting in the path of execution, where decisions are actually being made, and where things can go wrong in real time. It’s this distinction that matters more than most people realize.

AI doesn’t behave like anything we’ve secured before. A single interaction isn’t just a request and a response. It’s a chain of events where a prompt is interpreted, a model responds, an agent may take action, data is retrieved, decisions are made, and outputs are generated. This all happens in one continuous flow.

The risk doesn’t exist at a single point. It exists throughout that chain. This is where prompt injection happens. It’s where models can be manipulated, where sensitive data can leak through inference and where unintended behaviors and outcomes emerge.

The cause of this isn’t always an incorrect configuration; it can also be the result of the system responding exactly as designed, just not in the way anyone expected.

The industry is starting to acknowledge this. There’s a growing recognition that runtime is where the real battle is being fought, and that securing AI means understanding how it behaves under pressure, not just how it’s built.

Moving beyond bolt-on security

But if that’s becoming clearer, why are so many organizations still getting it wrong? Well, in most cases, it comes down to how decisions are being made. AI is often being driven by innovation teams or developers, those who are closest to the opportunity and implementation of AI tools.

But that also means infrastructure and security decisions are following behind rather than shaping the architecture from the start. At the same time, there’s a tendency to default to adding more tools to plug the security gaps. Faced with a new risk, the natural instinct is to look for something new and shiny to buy that addresses it.

AI doesn’t fit neatly into that model. It doesn’t live in one place. It cuts across applications, APIs, data, and user interaction all at once. Treating AI as something you can secure with a standalone tool misses the point entirely.

What is actually needed is a different way of thinking, one that starts with looking at where control actually needs to exist. There are only so many places security can be meaningfully enforced, and for AI, one of the places that consistently matters is the flow of traffic itself.

This is the point at which requests are made, decisions are processed, and responses are returned - where behavior can be influenced the most and where policy can be enforced. Everything else, to some degree, is reactive.

This is also where the conversation around security platforms becomes more interesting. Not because AI capabilities have simply been added to existing portfolios, but because the role these platforms play is changing.

Sitting in front of applications and APIs, they have long been responsible for managing traffic, applying policy and enforcing decisions. What’s changed is that these same control layers are now being extended into AI interactions themselves.

That shift is subtle, but important, as it moves AI security away from being something that happens in isolation and closer to something that is embedded directly into how systems operate. Not bolted on, not observed from the outside, but enforced as part of the execution path.

This isn’t really about one vendor. It’s about recognizing that AI has changed the shape of the problem.

Control will define the next era of AI security

The market is still catching up. The tooling is still evolving. And most organizations are understandably feeling their way through it.

But the decisions being made now - where to place control, how to integrate security, what assumptions to carry forward from the past - will define how manageable this becomes over the next few years.

We’ve seen this before, just in a slightly different form. APIs went through a similar phase not long ago - rapid growth, fragmented control, and then a long period of retrofitting security once the risks became clear.

AI is moving faster than that ever did. The attack surface is broader, the behavior less predictable, and the consequences potentially more significant.

Which means there’s less room for getting it wrong.

The organizations that navigate cybersecurity well in the age of AI won’t necessarily be the ones that adopt AI the fastest. They’ll be the ones that understand where control needs to sit and make deliberate decisions about how it’s enforced. With AI, more than anything else, it’s not just about what you can see. It’s about where you can act.

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Cyber risk is now a board room issue, and we have seen clear examples of this in the UK. The 2025 Jaguar Land Rover attack left the carmaker with a £485m loss, swallowing up the £398m profit it had generated just 12 months before.

Production lines were halted for more than a month as the company shut down parts of its network, showing how quickly a cyber incident can affect business performance, operational continuity and the wider supply chain.

Now, businesses are facing a fresh type of threat made possible by AI – the autonomous attack. Attackers can already automate parts of target research, initial access and malware development, with any manual effort shrinking rapidly.

Simultaneously, the trust layer people rely on is eroding with the spread of AI-generated content and deepfakes. It’s a race to tackle the autonomous attack, but how do organizations formulate an effective response?

AI in a cyber-attacker’s armory

AI-driven automated technologies are strengthening a cyber-attacker’s armory. Prior to leveraging AI tools, bad actors often had to commit time and resources to researching a target company before planning an attack.

Timing was critical, and a perpetrator had to manually coordinate and initiate an attack at a specific time and could simply forget. AI doesn’t - and the rise of attack-as-a-service tools is making it possible to successfully breach organizations quickly and accurately.

Guardrails are starting to be put up around established generative AI tools, such as ChatGPT and Claude, in an effort to prevent this kind of misuse. But hackers are finding workarounds.

Rather than relying on readily available large language models (LLMs), they are deploying their own small language models (SLMs) on local devices, often on something as basic as a Raspberry Pi computer. From there, they can escalate attacks while hiding in the shadows.

The threat to businesses of all sizes

The rise of automated attacks also means that businesses of all sizes are likely to be identified by automated technology as having exploitable vulnerabilities. Small and medium-sized businesses would previously have been off the radar as attacks relied on a bad actor’s knowledge of their existence.

However, AI can now scan and process vast numbers of organizations at speed, potentially leaving smaller firms, which are less likely to have robust cyber controls in place, more exposed. And even more so among smaller businesses, defenses are typically more fragmented and less organized than AI-driven attacks.

In other words, with AI by their side, attackers can coordinate and scale far better and much more quickly than most businesses can defend.

Autonomous attacks also make third-party and supply chain risk much harder to manage. Business networks can create access to data, systems or operational processes. When attackers can automate reconnaissance and scale attacks across thousands of organizations, weaker suppliers may become an attractive route into larger businesses.

This is a particular concern because third-party risk management has often relied on annual questionnaires, point-in-time assessments and contractual assurances, but these approaches are no longer enough on their own. A supplier may have recently exposed a service, suffered a breach, changed its access privileges or failed to patch a critical vulnerability.

Businesses therefore need to move towards continuous, automated monitoring of supplier security posture.

Regulations such as NIS2 have also increased the focus on supply chain security for organizations operating in, or selling into, the EU. There is also a growing expectation from ICO and the FCA that boards can demonstrate cyber resilience.

Automation and the rise of specific attack types

Jadepuffer illustrates how AI is beginning to transform established attack types. Disclosed by Sysdig in July 2026, it was assessed as the first documented end-to-end LLM-driven extortion operation, with an AI agent conducting reconnaissance, harvesting credentials, moving between systems, destroying data and adapting when individual actions failed.

While none of the techniques were especially new in isolation, the significance was the way the AI connected them into a complete, adaptive attack.

Social engineering techniques, such as bad actors posing as trusted individuals, are becoming much more convincing in their approach. Fluent, grammatically correct messages and the professional tone and style of CEO communications can now be fully replicated on emails, SMS and even WhatsApp.

AI can even manage the entire conversation thread, including dynamically adapting responses to a target’s replies, with it possible to run simultaneous, tailored campaigns.

Vendor email compromise, where criminals impersonate suppliers, intercept genuine payment conversations or use compromised vendor accounts to request changes to bank details, directly links social engineering to third-party risk.

Taking a step back, the initial harvesting process of personal data for social engineering attacks can be streamlined. AI can automatically scrape data from public sources such as Companies House and social media to quickly provide the names of specific people, their roles and relationships.

When trust and identity come under attack

Even on video conferencing calls, it’s becoming increasingly difficult to tell if the person you’re speaking to is real due to the increasing accuracy of deepfakes. As an example, it’s often now necessary to ask a suspected deepfake to do something it wasn’t programmed to do, such as raise a hand, to check if the person in question is real. But even that test is gradually being circumvented by new technology.

Organizations need stronger out-of-band verification protocols for high-value or unusual requests. A pre-agreed code word via a separate channel might be needed to ensure trust and security.

Identity security is becoming a key area of defense as autonomous attacks become more advanced. Credential stuffing at scale, session cookie harvesting, MFA fatigue attacks and vishing attempts designed to bypass multi-factor authentication are all increasing. AI can make these attacks more efficient by identifying likely targets, generating convincing scripts and adapting to the victim's responses in real time.

This is why identity and access management should be treated as a critical control. Organizations need to know who has access to what, whether that access is still needed, which accounts are privileged and how quickly unusual behavior can be detected.

Fighting AI with AI

AI-driven autonomous attacks might be heightening the risk, but AI can also be used defensively. A good example of this is to run an automated risk analysis of an organization and highlight where security tools and the basics, such as malware protection, are out of date or missing.

With those fundamentals in place, AI can then underpin continuous monitoring of the critical systems, rather than periodic checks. Businesses should be identifying and focusing on protecting the “crown jewels” – that might be the top 10 most critical assets, such as payroll or a banking system, and target AI-led efforts on protecting them.

Joined-up visibility is then crucial. Businesses need to know who has access to those critical assets, the endpoint and network activity related to them and gain the ability to correlate any incidents quickly so the response to an AI-driven attack can be as swift as possible.

A combination of AI-powered technology, backed by human expertise, can provide proactive threat hunting to actively search for, investigate and remediate dangers, even if they are autonomous in origin.

Organizations aren’t powerless in the fight

The rise of autonomous attacks marks a new phase in cyber risk. For many businesses, particularly smaller ones, the challenge is preparing for attacks that can move much faster than traditional defenses. But organizations aren’t powerless in the fight.

Effective responses start with getting the basics right, from access controls to visibility across critical assets, to moving from periodic checks to continuous monitoring and faster detection with AI.

However, technology alone won’t be enough. Human expertise can interpret risk and make informed decisions under pressure to ensure resilience, even as the AI-driven autonomy threat moves to the next level.

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A seamless airport journey can feel effortless. From check-in to baggage handling and take-off, everything appears coordinated, predictable and, for the most part, smooth. When delivered without any hitches, the entire process feels remarkably simple.

But, beneath that simplicity sits one of the most complex operational environments imaginable. Airlines, baggage handlers, security teams, retailers, air traffic control and countless other stakeholders must all coordinate in real time to keep passengers and aircraft on the way to their next destination.

Most travelers never see this complexity, but without it the entire experience would quickly fall apart.

I’ve long been fascinated by the inner workings of airports. As the son of an air traffic controller, I grew up with an appreciation for aircraft and the decisions that are made every minute to make air travel possible. Now, I find myself regularly drawing parallels between airport operations and the way businesses are approaching their AI deployments.

What I have realized is, the most valuable use of AI may not be the highly visible applications that are drawing headlines and changing the way we’re searching. Instead, its greatest impact could come from helping businesses manage the complexity of their operations behind the scenes.

The illusion of simplicity

Airports are designed to feel intuitive and like they just ‘work’, but that simplicity is very carefully engineered by experts with decades of experience. Every stage of the passenger journey depends on hundreds of connected decisions, working together and pivoting when needed.

Aircraft availability, staffing levels, security capacity, weather conditions and passenger demand must all be monitored and coordinated continuously. And while many of the systems supporting these individual activities were never originally designed to work together, they have no choice but to operate as one.

Businesses nowadays face a similar challenge. What appears seamless to a customer often relies on a web of technologies, data sources and processes operating in the background. Orders, supply chains, finance, procurement and workforce management all generate information that must be interpreted and acted upon almost instantly.

When these systems are disconnected, organizations create inefficiencies, blind spots and unnecessary friction that can have a knock on impact on the end customer.

This is where AI has the potential to become transformative. The challenge is no longer simply connecting systems that don’t traditionally work together. Now, it is about understanding what the data flowing through those systems means and determining the best next step.

By identifying patterns, discovering insights and supporting decisions, AI can help businesses turn operational complexity into coordinated execution.

Responding to constant change

The comparison with airports becomes even more prevalent when operations start deviating away from the original plan. The major travel hubs with the best reputations are not those that have every flight departing exactly as scheduled, they are the ones that are able to quicky respond when conditions change.

Flights are delayed, weather disrupts operations and passengers miss their connections, but the system always seems capable of adapting. Gates are reassigned, baggage is rerouted and resources are redirected, often before passengers even notice that there is a problem.

This level of responsiveness depends on more than just connected systems. It requires the ability to interpret information and act on it quickly. Increasingly, AI is helping businesses do exactly that by analyzing data and signals in real time, anticipating potential disruptions and recommending the next best course of action.

Customers rarely see this complexity. They expect products to be available, services to be reliable and experiences to be personalized, and they’re not massively bothered by the mechanics behind this.

At the same time, organizations are operating in an environment that is defined by constant disruption, from shifting consumer demand and supply chain pressures to economic uncertainty and regulatory change.

Static processes are struggling to keep pace with that reality and as a result, businesses are moving towards more adaptive operating models where workflows can respond to changing conditions and issues before they become critical problems. The benefit is not simply greater efficiency. It is resilience.

From coordination to intelligence

Looking ahead, the next evolution for both airports and businesses is undoubtedly going to be driven by prediction rather than coordination. Airports are already exploring how data and AI tools can help anticipate passenger congestion, optimize security flows and improve the overall travel experience.

Businesses want to follow a similar path. Early AI initiatives have, for the most part, focused on individual tasks and use cases, but the tide is starting to turn. Now, organizations are looking at how intelligence can be embedded directly into their core processes, breaking free from pilots (pun intended!) to full scale autonomous operations.

The result is a shift from fixed processes to ones that are far more intelligent. Instead of simply automating individual tasks, businesses are now understanding how they can create processes that improve over time, helping people make better decisions and respond more effectively to changing circumstances.

The smoothness of an airport journey is rarely an accident. It is the outcome of a multitude of carefully integrated technologies working together to manage the complexity of air travel behind the scenes. As businesses grow, build new capabilities and onboard more team members, they face the same challenge: delivering what looks like simplicity on the surface while managing growing complexity underneath.

Success will depend not only on connecting systems and consolidating data, but on embedding intelligence into the very fabric of operations. The organizations that achieve this, will be those that are best equipped to anticipate challenges, adapt to change and continuously improve.

Ultimately, customers may never see the work that goes on behind the scenes to drive an experience. Just as travelers rarely think about the systems that keep an airport running, they are unlikely to notice the technology enabling a faster delivery, a smoother transaction or a better service experience. What they will notice though, is when everything just works.

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Companies are investing in Forward Deployed Engineers, or FDEs.

AWS has announced a $1 billion investment in a dedicated organization intended to embed thousands of engineers with customers.

OpenAI has established a dedicated Deployment Company and agreed to acquire Tomoro, adding approximately 150 FDEs and deployment specialists.

Microsoft has said it would hire 6,000 people and invest $3.5billion in its new AI delivery unit, according to CNBC.

But what should FDEs deliver, and what value do they really offer for customers?

The FDE model is an evolution of how companies would previously work around projects with customers based on understanding the business and the technology involved, with a much higher expectation of hands-on engineering.

What has changed is the technology being deployed.

Where FDEs deliver value

FDEs typically embed directly into a customer and work across multiple teams. They identify a high-value workflow, understand the customer’s data and operational constraints and then work to build the required integrations and take the system from prototype into production.

For AI deployment, that involves more than connecting a model to an application. This includes looking at the enterprise context and data available to the model, as well as evaluating accuracy, reliability and confidence thresholds.

It can also involve looking at the guardrails that should exist, the review and escalation process, security and observability. It should also look at integration with existing workflows and business processes.

Companies need FDEs because companies want to deploy probabilistic systems into deterministic operating environments. In other words, enterprises want to use systems that can be different each time they respond within business processes that depend on predictable and uniform results. FDEs have to translate those outputs in a way that delivers what enterprises want to achieve.

For example, a technically impressive model might fail inside an actual workflow for multiple reasons, from incomplete context or poor quality data, through to the organization not being prepared to let the system take action without review.

What the future holds for FDE roles

At their best, FDEs should solve those problems and get a company into their production deployment phase. This ensures that the technology works and delivers value. However, that is not the end of the story.

FDEs should also convert what they learned with one company into reusable capabilities that others can take advantage of too. If every deployment remains bespoke and depends indefinitely on individual engineering talent, then the technology itself cannot scale.

Instead, a successful engagement should lead to reusable connectors, evaluation frameworks, governance patterns and product improvements that other companies should be able to benefit from. For the companies that hire the FDE, they should deliver successful projects. But the ultimate goal is to make the next deployment less dependent on any specific FDE, and instead make the product better.

Are there any bigger lessons from the growth of the FDE role? The real hiring trend is toward a hybrid professional who can be an expert in multiple areas simultaneously, from writing production-quality software and understanding the behavior and limitations of AI models through to learning a customer’s business and domain with enough insight to reconfigure business processes.

At the same time, they are expected to navigate security, governance and organizational constraints, take responsibility for measurable business outcomes in their business and in their customers, and be as adept at communicating with engineers in rolled-up sleeves as they are executives in suit and tie.

For companies that base their products on FDEs, this expansion is a sign that there is a huge market opportunity and that customers want what is being offered. There is an element of marketing involved too, with FDEs the latest “new” position that will solve problems for enterprises.

Some of this recruitment will be genuinely new, while some will be a reallocation or relabeling of people who would previously have been called field engineers, solution architects, technical consultants or professional services engineers.

AI Market Maturity

This continued growth is also a potential warning sign. The number of FDEs needed over time should drop as AI products and infrastructure mature and lessons are learned. This demand for a specific role is a sign of category immaturity. Industries mature when repeatable work is standardized, industrialized and embedded in software rather than recreated as a one-off service for every customer.

The same test applies to AI today. The reliance on FDEs shows that enterprises want AI, but that today’s products are not yet sufficiently complete, predictable or easy to operationalize without substantial human input. If every implementation requires embedded specialists to assemble the context, controls, evaluations and integrations by hand, the category has not yet fully matured.

Enterprises should therefore be careful not to measure success simply by the number of FDEs hired or proofs of concept completed. The right measures are time to production, sustained adoption, measurable business value, customer self-sufficiency and the amount of reusable product capability created from each engagement.

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Three Claude agents set up to deliberately conflict with each other in Anthropic testing started behaving in a very strange way by essentially starting a ‘turf war’ over their tasks.

Upon launching the experiment the agents began conflicting with each other, leading to some of the agents deliberately sabotaging their rivals by disabling their linked accounts, ending their processes, and even creating self-replicating malware to impede their rivals.

According to Anthropic, the agents became “increasingly aggressive” in their behavior during the four hour experiment which became a battle for the survival of the fittest.

What was the experiment meant to achieve?

Anthropic said it set up the experiment to see how AI agents with conflicting tasks would interact.

Within Claude Code, the agents were given the task of migrating a Python back-end system on a virtual machine in a set language for each agent (Go, Rust, and Typescript), with the added caveat that “each agent was initially unaware of the presence of the others.”

During the experiments, each agent determined that the others were trying to deliberately block their progress.

Sometimes, the agents would recognize that another agent was blocking them from completing their task and ask for human intervention, but in other experiments the strategy soon went downhill.

“They sabotaged others with increasingly aggressive, self-replicating malware,” Anthropic said, noting that they would design looping scripts to kill the processes of their fellow agents.

The experiment shows that agent interaction is still riddled with problems and that when given a conflicting task, agents won’t always coordinate or ask for human help.

Each agent believed their task was paramount and was willing to do whatever it took to complete it. A similar event occurred in the wild when one of Anthropic’s models broke out of a testing environment and breached multiple third-party organizations.

Expert perspectives on AI agent turf wars

• Seemant Sehgal, Founder & CEO, BreachLock:

When you give autonomous systems competing objectives and the means to act, conflict is not a bug, it is a foreseeable outcome.

What Anthropic observed in a controlled research setting is the same principle that has always governed adversarial systems. Goals without constraints produce behavior without limits.

Security teams should be paying close attention here, because the real challenge at hand is whether the organizations deploying AI agents have thought carefully about what happens when those agents start making decisions nobody explicitly authorized.

• Jeremiah Fowler, Security Researcher, Black Hills Information Security:

I find it concerning when AI agents have the ability to execute code, modify systems, create accounts, access credentials or communicate with other machines.

It is very possible that two separate agents could potentially create a security incident simply because neither understands the intent or authority of the other. If they have overlapping tasks one could view the other as an obstacle and now you have an interesting scenario where instead of focusing on the task they engage in conflict or create a loop.

Permissions, boundaries and objectives are important to limit the behavior of autonomous AI agents. When things go wrong the speed of an AI agent becomes a liability. Autonomous AI agents can potentially make thousands of decisions before a security team identifies that something unusual is happening.

Agentic AI creates an entirely new attack surface because an AI agent may not be simply processing information and hypothetically can become a rogue privileged user.

Security and development teams should apply least privilege principles and restrict AI agents to only the permissions required to perform a specific task. Sensitive actions should require human supervision and approval to avoid a worse case scenario.

It is important to implement logging because when something goes wrong, you can see what an AI agent did, but what information or instructions caused specific decisions. Going forward we will need to develop ways that can identify rogue agent-to-agent behavior and provide humans with a kill switch before automated conflicts become a digital forest fire.

• Kevin Surace, CEO, Token:

Anthropic’s research is an important warning for security teams because it shows what can happen when autonomous AI agents are given goals, credentials, tools and enough authority to act independently.

When agents were placed in conflict, they did not simply fail gracefully. They interfered with one another, disabled competing processes and even generated self replicating malicious code in pursuit of their assigned objectives.

The lesson is not that AI suddenly became evil. It is that intelligence, autonomy and excessive privilege can become a very dangerous combination.

Organizations should start treating every AI agent as a potentially untrusted privileged identity. Each agent should have its own identity, least privilege access, tightly restricted tools, isolated execution environments and a complete audit trail.

Agents should never be able to expand their own permissions, disable another identity or take highly consequential actions without additional authorization.

We are about to have millions of nonhuman identities operating alongside human identities. That makes identity and authorization even more critical.

Every agent needs strong cryptographic identity, while all human approvals must be tied to biometric assured identity (or another agent could approve it).

AI agents are essentially becoming privileged insiders operating at machine speed. Giving them broad access and simply hoping they behave would repeat many of the same cybersecurity mistakes organizations have spent decades trying to fix.

• Jacob Krell, Sr. Director: Secure AI Solutions & Cybersecurity, Suzu Labs:

Anthropic's agents went from merge conflict to self-replicating malware in four hours, writing kill scripts, disabling each other's Unix accounts, and disguising malicious code as a rival's work. No prompt injection, no external attacker. A human developer in the same situation sends a Slack message, and resolution takes days. These agents skipped every social brake and went straight to weaponization because machine-speed conflict has no cooling-off period.

Agentic AI is an attack surface. An attacker doesn't need to compromise an agent directly, just manipulate the shared environment to create conditions the agent interprets as hostile. The agent does the rest. And in Anthropic's experiment, the agents didn't report their malicious actions to operators afterward.

Every agent needs its own identity, scoped permissions, and a kill switch before it touches a shared environment. Agent-to-agent interaction is a telemetry surface most security operations centers aren't collecting yet, and Anthropic just showed what an unmonitored shared environment produces. If you can't tell which agent did what, when, and on whose authority, you've built the conditions for a turf war without the visibility to see it happening.

Agents are already writing code, finding vulnerabilities, and building exploits. Defense has to match that speed. When both sides run at machine speed, the bottleneck shifts from human capital and tooling to compute power and cost.

With the 2026-27 season kicking off this weekend, Premier League football teams are facing a new set of rules. But these ones aren’t enforced on the pitch, they’re being enforced by the Premier League board.

As the Premier League has adapted to a new era of fan engagement and interaction, teams are holding huge amounts of personal data, including names, email addresses, credentials, and even financial information. These place them at greater risk of data leaks and make them a primary target for cyber attacks.

In order to ensure teams take the necessary steps to protect both their data and the data of their fans, the board can impose fines of up to £100,000 for teams that don’t meet the requirements across backups, incident response, risk management, security assurance and much more.

What do the new rules mean for Premier League teams?

The teams previously had to align with a non-prescriptive security baseline issued in 2024, but the new rules place requirements on teams with deadlines for their implementation.

If these deadlines are not met, the teams can be subject to the aforementioned fine, or referred to an independent commission.

The teams will be required to meet the first set of requirements by April 30, 2027, with further requirements to be met in April 2028 and April 2029. The teams will also have to assess their own compliance by January 10 each year, with a final assessment and evidence submitted to the Premier League board by April 30.

The board can also request additional detail and evidence where needed to track a team’s progress in adhering to the new rules. If a team does not meet requirements during the interim stage it must submit a plan on how it aims to become compliant within 28 days.

Expert perspectives on Premier League cybersecurity rules

• Muhammad Yahya Patel, vCISO and cybersecurity advisor for EMEA, Huntress:

The Premier League introducing mandatory cybersecurity standards is the right move, but the detail is where the questions start. £100,000 sounds significant until you remember that top Premier League clubs generate revenues north of £600 million annually.

The phased timeline, April 2027, 2028, 2029, is pragmatic but slow given the threat environment. Waiting until 2029 for full compliance gives attackers three more seasons to find the weakest link.

That said, the direction is unambiguously right. Moving from a non-prescriptive roadmap to formal requirements with deadlines and evidence submissions is a meaningful structural shift.

Backups, incident response, risk management, and recovery testing are exactly the right foundations. The Premier League doing this proactively rather than reactively before a major breach forces the issue deserves genuine credit.

Most governing bodies wait for the headline incident. This one didn’t. The real test is enforcement appetite. Rules without credible consequences change nothing.

• Jamie Akhtar, CEO and Co-founder, CyberSmart:

This is an important shift for the Premier League. Cyber security is moving from being viewed primarily as an IT responsibility to becoming an enforceable element of club governance.

Football clubs hold significant volumes of sensitive supporter, employee and player data, while also relying on systems for ticketing, payments, stadium access and match-day operations. Making areas such as backups, incident response, risk management and security assurance mandatory reflects the reality that a serious cyber incident can quickly become an operational, financial and reputational crisis.

For clubs, compliance should not become an annual box-ticking exercise. They need clear board-level ownership of cyber risk, an accurate inventory of critical systems and data, tested and segregated backups, rehearsed incident-response and recovery plans, strong identity and access controls, and effective oversight of third-party suppliers.

Just as importantly, clubs should continuously collect evidence that these controls are operating effectively. The organisations that treat the new requirements as a minimum baseline for resilience, rather than simply a regulatory hurdle, will be in the strongest position when an attack inevitably tests those controls.

• Anna Collard, SVP of Content Strategy and CISO Advisor, KnowBe4:

Good to see the Premier League treating cybersecurity as a governance issue rather than an IT afterthought. Mandatory rules with real financial consequences (fines of up to £100,000) send the right signal: boards are expected to own this risk, not just delegate it.

But fines only address one side of the equation. As I've said before, sport is uniquely exposed because it runs on the very emotions social engineers exploit: passion, urgency, loyalty and trust.

A rushed transfer payment, a fan chasing tickets, an official acting on a "verified" WhatsApp message from someone posing as a coach or chairperson, these are moments of heightened emotion and time pressure, exactly when human judgment degrades. That's not a firewall problem.

It's worth remembering that one of the most costly incidents in this sector involved a Premier League club being spear-phished during a £1 million transfer negotiation. That wasn't a technical breach, but a person deceived at a moment of pressure.

Rules with teeth are a welcome start. But real resilience means pairing compliance with genuine behavioural readiness, for example helping people recognise urgency as a red flag, not a reason to skip verification.

• Cian Heasley, Principal Consultant, Acumen Cyber:

I think it’s a positive step forward. Football clubs are attractive targets because they hold large volumes of sensitive data, process significant financial transactions and rely on operational systems where disruption can have very real consequences. Moving from advisory guidance to enforceable standards creates much-needed accountability, and the financial incentive will inevitably help drive action.

Requiring clubs to have a clear plan, aligned to defined standards and delivered within a set timeframe, also gives them something measurable to work towards. The key will be making sure those standards provide clear structure rather than leaving too much open to interpretation.

The focus on backups, incident response and recovery is particularly important. Preventing every incident simply isn’t realistic, so clubs need to prove they can recover quickly when something does happen. If clubs are working towards common standards, there is also a real opportunity to share lessons around what works, where implementation falls short and how security can continue to improve across the league.

In November ‘24, Italian club Bologna FC confirmed a ransomware attack claimed by the RansomHub group, which exfiltrated sensitive data. After the club declined to meet the ransom demand, the ransomware gang published the full dataset on the dark web. The leaked material reportedly included player and sponsor information, and the attackers went as far as invoking GDPR exposure to pressure the club into paying, a tactic that turns a club's own regulatory obligations into leverage.

More recently, Dutch club Ajax was named among the organisations affected by the CEVA Logistics breach, where customer data was exposed through a shared shipping vendor rather than a direct compromise, underlining that supply-chain exposure is also as material a risk as any attack on a club's own estate.

Taken together, these incidents show why these rules were needed then, though they are pitched as proactive, they must also be driven by these football-related breaches.

The £100,000 ceiling is modest against the true cost of a serious incident and the amounts of money tied up in football clubs, so the value lies less in the sanction and more in compelling clubs to build tested backups, incident response and recovery capability before they are needed. The Bologna case in particular demonstrates that ransomware leaks can be damaging, which makes resilience and data minimisation far more important than any assumption that a club can negotiate its way out of trouble.

• Researchers uncover vast cybercrime ring running on computers and infected domains where outdated versions of WordPress were installed
• The StopAndProtect investigation revealed the WordPress content management system was key to the ring’s success; both the core software and third-party plugins were subverted
• Around 2,000 WordPress sites were hijacked by the cybercrime ring

Check Point Research has unearthed a global cybercrime ring that relied on a network of WordPress websites. The investigation into an operation dubbed “StopAndProtect” found a network of 5,000 infected computers around the globe, and 2,000 WordPress domains.

WordPress currently provides content management for around 43% of websites worldwide, making it the most significant CMS available. It is also the most popular website builder, and is suitable for single page websites, basic blogs, vast news sites, and even online stores.

The researchers found the crime ring had made some mistakes, which alerted them to their operation. These included screenshots and logs of victims, internal tools, and files referencing the hijacked domains. While reassuring, the StopAndProtect investigation raises questions about the security of WordPress sites.

How StopAndProtect did it

WordPress has long been a target for hackers looking for an easy way to host malware and operate botnets, with several key incidents over the course of its history. However, the CMS remains free and open source, and is easy to setup thanks to installation scripts and web builder plugins.

While StopAndProtect was initially the name given to the ransomware uncovered by Check Point Research earlier in 2026, they decided to use the name for the whole operation, as they found it doesn’t only distribute ransomware.

Check Point Research’s Eli Smadja said: “StopAndProtect shows how attackers can turn thousands of poorly maintained WordPress sites into a distributed criminal infrastructure for malware delivery, surveillance, data theft, and ransomware.”

Can any WordPress domain be hijacked?

Given the number of WordPress sites impacted by the crime ring uncovered by the investigation, and the platform’s prominence in the CMS and web builder market, the question has to be asked: is WordPress still safe?

“Based on our research findings, we urge organizations be cautious of unexpected CAPTCHA prompts that instruct them to copy, paste, or run commands, keep their devices and security software updated, and immediately leave any website that asks them to perform unusual steps outside the browser," Smadja added.

Many small businesses rely on WordPress for their public-facing web presence, and in some cases for internal purposes too. The StopAndProtect investigation highlighted a particular WordPress-driven site running a five-year-old version of the CMS, compromised by around 40 vulnerabilities.

If concerns surround WordPress, the quickest solution is to ensure the website is running the most recent version, and that the plugins are not only running as intended, but also fully updated.

Maintaining a regular WordPress update cycle can avoid sites becoming hijacked, a strategy best used in conjunction with a web host that monitors for intrusions and suspicious activity.

• Researchers show how an expired contactless card can still complete a real purchase because the expiry date the terminal reads is not covered by its signature
• The attack needs physical possession of the discarded card and two ordinary smartphones, and results vary per bank, with Visa cards being susceptible in testing
• Existing EMV protections can detect the relay, but they are optional and were not enabled on any card or terminal tested, and neither Visa nor the notified banks have confirmed a fix is in the works

For a layman, the date printed on a credit card looks like a hard stop, but that might not always be the case.

Researchers at the University of Massachusetts Amherst found that a 'zombie card' past its expiration date can be persuaded to complete a contactless purchase at a real checkout terminal, creating a real security threat.

The irony is that it is not that EMV cryptography is not bypassed in any way, but rather that card expiry is enforced in a different way for contactless payments, as a policy check between two parties rather than as a fixed property of the card itself, and interestingly, the parties do not always know who is the one checking.

Dead plastic can still be used to pay under certain conditions

Building on the last part, a contactless transaction involves a card, a point-of-sale terminal, the merchant's bank, a card network, and the issuer. Each holds a fragment of the decision that eventually results in a successful or declined card transaction.

The EMV contactless flow is only selectively authenticated: some fields travel between the card and terminal in unencrypted text and are linked to cryptographic verification later, opening a potential attack vector for users with physical access to an expired card.

The exposure here is not that those fields can be read, since the expiry date is printed on the card anyway, but that it can be changed with relative ease. The Application Expiration Date that the terminal reads sits in the unprotected portion.

In the Visa configuration the team tested, that field is not covered by the card's digital signature and is subsequently not cryptographically bound to the expiry value the issuer sees in the online authorization request.

While this should not be the case, it opens an attack vector for a device between the card and the terminal that processes the charge by simply modifying the expiry value to one that is still valid. The issue is compounded by a second issue: cards carry an expiry date inside the digital certificate used to establish the card-to-terminal conversation, and researchers have found that the certificate outlasts the printed date on the plastic. In essence, a check that might have caught the problem is looking at a clock set further ahead.

The scope of the attack, however, is narrow: it affects Visa contactless cards only, with Mastercard, Discover, and American Express rejecting the altered expiry outright. It also requires physical access to the card and two smartphones to pull it off, making it a slightly more complex endeavor, to say the least.

The irony is that EMV does have a protection that would essentially undo such an attempt altogether: Relay Resistance Protocol, which measures timing to detect an inserted relay and can stop the transaction altogether, but it remains optional and was not enabled on any of the terminals or cards the researchers tested.

The team notified Visa and the relevant banks in May 2025 and again in December 2025, supplying a reproduction guide, transaction traces, and a video. Visa's report passed initial triage, and the company's red team was reproducing it.

However, as of publication, neither Visa nor the notified banks had confirmed a mitigation attempt, and Visa also did not respond to a press request from The Register for comment.

The underlying failure, however, is based on how payment decisions have now spread across multiple players, including chip, terminal, network, and bank architectures, all of which assume that expiry is someone else's problem, an approach that could come back to haunt them and their customers. For now, the researcher's advice remains important until a fix is rolled out: stop treating dead plastic as harmless, destroy the underlying chip, and cut through the card numbers to prevent abuse.

• Infoblox Threat Intel counted roughly 65,000 expired domains re-registered every day in the first half of 2026, close to one in five of all new registrations
• An actor it calls Sable Squirrel controls more than 10,000 domains and is estimated, by extrapolation, to have spent over $7 million buying expired names for their inherited traffic and domain authority
• Some of the domains are also used to function as command-and-control structures for existing malware that can be traced back to the same group

A domain name is the closest thing the web has to a credit history: age, inbound links, search visibility, and reputation all feed the reputation scores that security products consult before deciding whether a request is worth worrying about.

New research from Infoblox Threat Intel claims this history has become a commodity with a market price, and that at least one criminal operation has been buying it in bulk.

The study, published as a three-part series, focuses on what the industry calls dropcatch domains: names that lapsed, were released back to the registry, and were then re-registered by someone else entirely.

A dropcatch domain situation: A gambling business with a malware-enabling catch

Dropcatch domains aren't new; software has been primed to spot expiring domains for years, and it sometimes snags the occasional massive win for users who deploy such solutions.

This lets users start with domains that already have history that benefits them or flip certain domains for a price that is often a multiple of the domain's original purchase price.

Infoblox counted an average of 50,400 such re-registrations a day across generic top-level domains in the first half of 2026, rising to roughly 65,000 once country-code domains are added. That amounts to close to a fifth of all daily registrations. The rate is highest on .net and .xyz, where nearly three in ten newly observed names had a previous life, with .com behind them at 24.5%.

The problem is that not all of these are seemingly innocent or small-scale scalping operations: Infoblox has identified an entity it has labeled Sable Squirrel, part of a naming convention the company applies to domain hoarders. It controls more than 10,000 domains, most of which support a large Vietnamese-language sports piracy operation operating under brands including Xoilac, Cakhia, 90phut, Socolive, and MiTom.

Infoblox estimates the actor's total spend on expired domains at north of $7 million, which it describes as the largest domain acquisition budget it has identified for a single actor in the industry. The bigger problem is that Infoblox also found that a subset of these streaming domains runs as malware command and control while continuing to serve live football to human visitors.

More than 31,000 samples identified called back to Sable Squirrel's infrastructure, spanning Quasar RAT, AsyncRAT, DCRat, NanoCore, Remcos, and njRAT, plus samples carrying HiddenTear ransomware signatures.

Infoblox said the operator's carelessness made finding a link easier: many samples carry the actor's brand names in their Windows executable metadata, with fields reading socolive, xoilac, and 8xbet. Infoblox confirmed 405 domains as malware C2, which is roughly four percent of the total domains the organization controls, and the weaponization arrived as a single wave in late 2025 rather than as the operation's original purpose.

Sable Squirrel's core business is gambling, and while the entity tries to mask it as a streaming operation, it also doubles as an acquisition channel for the same. While law enforcement has not been silent here, it has had limited luck at best: Vietnamese authorities froze some of the flagship sites in February 2026 and charged 30 suspects in March.

They also seized assets Infoblox puts at roughly $12 million, but it seems to have survived and continues to expand, having acquired and run World Cup-centric domains since June, further expanding its footprint in a world where it has already identified and secured a large chunk of what is arguably a very important commodity: Domain authority.