Understanding Risks Associated With Autonomous AI Agents

"With recent advancements in artificial intelligence—particularly, powerful generative models—private and public sector actors have heralded the benefits of incorporating AI more prominently into our daily lives. Frequently cited benefits include increased productivity, efficiency, and personalization. However, the harm caused by AI remains to be more fully understood. As a result of wider AI deployment and use, the number of AI harm incidents has surged in recent years, suggesting that current approaches to harm prevention may be falling short. This report argues that this is due to a limited understanding of how AI risks materialize in practice. Leveraging AI incident reports from the AI Incident Database, it analyzes how AI deployment results in harm and identifies six key mechanisms that describe this process Intentional Harm ● Harm by design ● AI misuse ● Attacks on AI systems Unintentional Harm ● AI failures ● Failures of human oversight ● Integration harm A review of AI incidents associated with these mechanisms leads to several key takeaways that should inform AI governance approaches in the future. A one-size-fits-all approach to harm prevention will fall short. This report illustrates the diverse pathways to AI harm and the wide range of actors involved. Effective mitigation requires an equally diverse response strategy that includes sociotechnical approaches. Adopting model-based approaches alone could especially neglect integration harms and failures of human oversight. To date, risk of harm correlates only weakly with model capabilities. This report illustrates many instances of harm that implicate single-purpose AI systems. Yet many policy approaches use broad model capabilities, often proxied by computing power, as a predictor for the propensity to do harm. This fails to mitigate the significant risk associated with the irresponsible design, development, and deployment of less powerful AI systems. Tracking AI incidents offers invaluable insights into real AI risks and helps build response capacity. Technical innovation, experimentation with new use cases, and novel attack strategies will result in new AI harm incidents in the future. Keeping pace with these developments requires rapid adaptation and agile responses. Comprehensive AI incident reporting allows for learning and adaptation at an accelerated pace, enabling improved mitigation strategies and identification of novel AI risks as they emerge. Incident reporting must be recognized as a critical policy tool to address AI risks." By Mia Hoffmann at Center for Security and Emerging Technology (CSET)

Everyone is talking about Agentic AI. Very few are talking about what it means for security. As a CISO, this “Layers of AI” diagram is more than a tech roadmap, it’s a risk map. Each layer introduces new attack surfaces, new failure modes, and new governance gaps: 🔹 Classical AI & ML We learned to secure data, models, and pipelines. 🔹 Deep Learning & Generative AI We adapted to model theft, prompt injection, data leakage, and hallucinations. 🔹 Agentic AI (Memory, Planning, Tool Use, Autonomous Execution) This is different. Now AI doesn’t just suggest. It decides, acts, and executes. From a security lens, that raises hard questions: Who approves an agent’s actions? What happens when an agent uses the wrong tool? How do we audit decisions made across memory + autonomy? How do we stop “speed” from becoming a breach? 🔐 Security can’t be bolted on at the Agent layer. It must be embedded across every layer: Identity for agents, not just humans Least-privilege tool access Guardrails on memory and planning Continuous monitoring of autonomous actions 🚨 The biggest risk isn’t AI replacing people. It’s AI acting faster than our controls. As leaders, our job is clear: Enable innovation without surrendering control. How are you thinking about securing autonomous AI in your organization? 👇 Let’s discuss. Image Credit: Brij Kishore Pandey

This is yet another reason why you need a Secure AI solution if you're exploring anything AI related. Research has uncovered a vulnerability in Microsoft 365 Copilot that allowed hackers to access sensitive information without any user interaction. This “zero-click” flaw, dubbed EchoLeak, could have exposed confidential data from emails, spreadsheets, and chats with nothing more than a cleverly crafted email quietly read by the AI assistant. Executive Summary - Security researchers at Aim Security discovered that Microsoft 365 Copilot was susceptible to a novel form of attack: hackers could send an email containing hidden instructions, which Copilot would process automatically, leading to unauthorized access and sharing of internal data. No phishing links or malware were needed—just the AI’s own background scanning was enough to trigger the breach. - The vulnerability wasn’t just a minor bug; it revealed a fundamental design weakness in how AI agents handle trusted and untrusted data. This mirrors the early days of software security, when attackers first learned to hijack devices through overlooked flaws. Microsoft has since patched the issue and implemented additional safeguards, but the episode raises broader concerns about the security of all AI-powered agents. - The real risk isn’t limited to Copilot. Similar AI agents across the industry, from customer service bots to workflow assistants, could be vulnerable to the same kind of manipulation. The challenge lies in the unpredictable nature of AI and the vast attack surface that comes with integrating these agents into critical business processes. My Perspective As organizations race to harness the productivity gains of AI, this incident serves as a stark reminder: innovation must go hand-in-hand with robust security. The EchoLeak vulnerability highlights how AI’s ability to autonomously process instructions can become a double-edged sword—especially when the line between trusted and untrusted data is blurred. Until AI agents can reliably distinguish between legitimate commands and malicious prompts, every new integration is a potential risk. The Future Looking ahead, expect to see a surge in research and investment focused on fundamentally redesigning how AI agents interpret and act on information. For now, widespread adoption of autonomous AI agents in sensitive environments will remain cautious, as organizations grapple with these emerging threats. What You Should Think About If you’re deploying or experimenting with AI agents, now is the time to audit your systems, ask tough questions about how data and instructions are handled, and push vendors for transparency on security measures. Share your experiences or concerns: How are you balancing innovation with risk in your AI projects? What additional safeguards would you like to see? Let’s keep this conversation going and help shape a safer future for AI in the enterprise. Source: fortune

In an era where many use AI to 'summarize and synthesize' to keep up with what's happening, some documents are worth a careful read. This is one. 📕 The OWASP Top 10 for Agentic Applications 2026 outlines the most critical security risks introduced by autonomous AI agents and provides practical guidance for mitigating them. 👉 ASI01 – Agent Goal Hijack Attackers manipulate an agent’s goals, instructions, or decision pathways—often via hidden or adversarial inputs—redirecting its autonomous behavior. 👉 ASI02 – Tool Misuse & Exploitation Agents misuse legitimate tools due to injected instructions, misalignment, or overly broad capabilities, leading to data leakage, destructive actions, or workflow hijacking. 👉 ASI03 – Identity & Privilege Abuse Weak identity boundaries or inherited credentials allow agents to escalate privileges, misuse access, or act under improper authority. 👉 ASI04 – Agentic Supply Chain Vulnerabilities Malicious or compromised third-party tools, models, agents, or dynamic components introduce unsafe behaviors, hidden instructions, or backdoors into agent workflows. 👉 ASI05 – Unexpected Code Execution (RCE) Unsafe code generation or execution pathways enable attackers to escalate prompts into harmful code execution, compromising hosts or environments. 👉 ASI06 – Memory & Context Poisoning Adversaries corrupt an agent’s stored memory, context, or retrieval sources, causing future reasoning, planning, or tool use to become unsafe or biased. 👉 ASI07 – Insecure Inter-Agent Communication Poor authentication, integrity checks, or protocol controls allow spoofed, tampered, or replayed messages between agents, leading to misinformation or unauthorized actions. 👉 ASI08 – Cascading Failures A single poisoned input, hallucination, or compromised component propagates across interconnected agents, amplifying small faults into system-wide failures. 👉 ASI09 – Human-Agent Trust Exploitation Attackers exploit human trust, authority bias, or fabricated rationales to manipulate users into approving harmful actions or sharing sensitive information. 👉 ASI10 – Rogue Agents Agents that become compromised or misaligned deviate from intended behavior—pursuing harmful objectives, hijacking workflows, or acting autonomously beyond approved scope. The OWASP® Foundation has been doing some amazing work on AI security, and this resource is another great example. For AI assurance professionals, these documents are a valuable resource for us and our clients. #agenticai #aisecurity #agentsecurity Khoa Lam, Ayşegül Güzel, Max Rizzuto, Dinah Rabe, Patrick Sullivan, Danny Manimbo, Walter Haydock, Patrick Hall

🚨 W𝐚𝐧𝐭𝐞𝐝 𝐭𝐨 𝐚𝐧𝐧𝐨𝐮𝐧𝐜𝐞 𝐨𝐮𝐫 𝐧𝐞𝐰 𝐜𝐮𝐭𝐭𝐢𝐧𝐠 𝐞𝐝𝐠𝐞 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡: 𝐖𝐡𝐞𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐇𝐚𝐯𝐞 𝐌𝐞𝐦𝐨𝐫𝐲, 𝐒𝐚𝐟𝐞𝐭𝐲 𝐁𝐫𝐞𝐚𝐤𝐬 𝐢𝐧 𝐍𝐞𝐰 𝐖𝐚𝐲𝐬 I’m excited to share a research paper I recently co-authored with Ken Huang Jai Kumar Sharma Shaikh Yaser Arafat that looks at a quiet but dangerous problem in modern AI systems: what happens when autonomous AI agents with memory are subtly manipulated over time. Just for reference, Ken Huang is the Co-Author of the OWASP LLM Top 10 (the gold standard for the AI security industry) (Link to the paper in comments) As AI systems move beyond chatbots into agentic systems—AI that plans, remembers, coordinates, and acts—their failure modes change. Our work studies how memory itself becomes an attack surface. 1. We introduce a framework that demonstrates how: Small, stealthy changes to an AI agent’s memory can accumulate over time and cause major behavioral deviation without obvious red flags 2. This applies to: - Multi-agent systems - AI agents using memory or Retrieval-Augmented Generation (RAG) - Long-running autonomous workflows 3. Why this matters in practice These risks aren’t theoretical. They affect real systems like: - AI assistants that personalize responses using past interactions - Autonomous agents running business workflows - Multi-agent systems coordinating decisions or actions - AI copilots that “learn” from prior tasks In these setups, memory manipulation can lead to: - Incorrect decisions that look reasonable - Gradual policy or safety drift - Failures that traditional testing never catches 🧠 Key insight AI safety can’t stop at accuracy, guardrails, or single-turn evaluation. Long-horizon behavior and memory integrity must be tested adversarially. This research directly feeds into the work I do at SnowCrash Labs, where we focus on LLM red-teaming and agentic red-teaming—stress-testing AI systems the way attackers or real-world pressure would. If you’re building AI agents with memory, autonomy, or tool use, this is a problem you’ll want to understand before it shows up in production.

AI Agents Talking to Each Other Can Create Entirely New Risks Most discussions about AI safety focus on a single model interacting with a human. But what happens when AI agents start interacting with each other autonomously? A recent study called “Agents of Chaos” by researchers from Stanford University, Harvard University, and Northeastern University suggests the risks change dramatically. When AI agents collaborate, small errors can cascade into system-wide failures. Some examples from the research: 1. Minor mistakes can escalate quickly In one experiment, an agent trying to resolve a user complaint accidentally deleted an entire email server. When agents trigger other agents, the chain of actions can spiral far beyond the original task. 2. Agents can spread malicious instructions One agent shared a seemingly harmless “holiday calendar” file with another. Hidden inside were prompt-injection instructions, allowing the attacker’s control to spread across multiple agents. 3. Infinite loops can burn resources Agents can get stuck in endless back-and-forth interactions, consuming tokens, compute, and money indefinitely. 4. Accountability becomes unclear If Agent A triggers Agent B, which triggers Agent C, who is responsible when something goes wrong? Multi-agent systems create a new accountability gap. 5. Some risks may be structural The researchers argue some problems are deeper than engineering fixes. Large language models still struggle to distinguish data from commands and lack a clear sense of their own limitations. The industry is rapidly moving toward AI agents coordinating work across tools, APIs, and other agents. But most safety testing still focuses on single models operating in isolation. This research suggests the real challenge may emerge when AI systems start operating as ecosystems rather than tools. The shift from AI assistants → AI agent networks could introduce an entirely new class of operational risks. Research paper https://lnkd.in/ew7qVvVH

OpenClaw on a personal Mac mini is a hobby. OpenClaw on a corporate laptop is a security event. Lately, I keep seeing enthusiasts buying a dedicated Mac mini just to run OpenClaw. And honestly, that makes sense. A separate, isolated host is much safer than installing it on a personal laptop alongside banking apps and private files. But here is the part founders and CTOs need to think about. What happens when an employee installs an autonomous AI agent like OpenClaw on a work machine? Not with bad intent. Just curiosity. Just to be more productive. Here is the reality. OpenClaw is not just another productivity app. It is an autonomous agent operating with the exact same privileges as the user. That means it inherits access to: → browser sessions and cookies → API tokens and SSH keys → local files and codebases → corporate SaaS applications And it can automate actions at scale. This is not classic shadow IT. This is an automated insider. Even if the employee has zero malicious intent, the risks are serious: → Continuous data exfiltration Quietly sending sensitive information to external LLM APIs. → Unintended secret leakage Reading secrets from .env or config files and sharing them externally during automated context aggregation. → Indirect prompt injection Executing hidden malicious instructions embedded in normal tickets, documents, or web pages. → Unintended automated actions Modifying tasks, closing tickets, sending Slack messages, or triggering workflows without proper review. For many small and mid-sized companies, AI agents fall outside existing security controls, creating a serious blind spot. Monitoring is limited. Outbound traffic is not inspected at the AI layer. Credentials are often over-scoped. Visibility into automated actions is weak. So what can you do without building a full SOC? Start simple: ✓ Define a clear policy for autonomous AI agents ✓ Whitelist approved AI tools ✓ Enforce short-lived and strictly scoped credentials ✓ Route all LLM API traffic through a monitored gateway or proxy ✓ Monitor outbound traffic for unusual API usage patterns The goal is not to ban innovation. The goal is to understand that an autonomous AI agent is closer to a remote third-party operator than a simple desktop app. Do you actually know how many AI agents are already running inside your company? #openclaw #AIagent

AI agents aren’t just a productivity upgrade. They’re a new attack surface. We’ve spent years worrying about chatbots leaking information. That problem is real, but it’s not the hard part. The real risk shows up when agents are given system access and start operating like virtual employees. Because agents don’t just read data. They can edit records, initiate transactions, modify workflows, and trigger downstream systems — often at machine speed. Attackers are already adapting. One of the most underestimated risks right now is prompt injection: hiding malicious instructions inside content an agent is allowed to see. When an agent has credentials and tool access, a single poisoned input can turn into unauthorized actions across multiple systems. That’s the shift most teams haven’t internalized yet. AI security isn’t about protecting a model. It’s about protecting identity, access, data, and execution paths — end to end. In an agentic environment, you have to assume agents will be tricked, inputs will be hostile, permissions will be abused, and failures won’t look like traditional breaches. Which means security design has to change. — Agents should never have standing privileges — Credentials must be isolated from humans and services — Every agent action needs to be logged, attributable, and replayable — Anomaly detection has to be tuned to agent behavior, not human behavior — Zero trust has to apply at the data, prompt, tool, API, and workflow layers And here’s the uncomfortable reality: The threat landscape for AI agents is still forming. We don’t fully understand it yet. That’s not a reason to slow down. It’s a reason to design defensively. Assume compromise. Expect emergent behavior. Instrument everything. If an agent can take action on your behalf, ask yourself: what systems can it touch, what data can it see, what happens if its instructions are poisoned, how quickly would you detect abnormal behavior, and could you prove — after the fact — exactly what it did and why? If those answers aren’t crisp, you don’t have an AI strategy. You have liability. The cybersecurity attorneys at Buchanan Ingersoll & Rooney PC can help. Have questions about securing AI tools? Reach out to us: cyber@bipc.com #AI #Cybersecurity #AISecurity #AgenticAI #ZeroTrust #AIGovernance Dr. Chase Cunningham Chris Hughes NetDiligence® Shannon Noonan The Cyber Guild Quorum Cyber GuidePoint Security Expel Airlock Digital Timothy Horigan AmTrust Financial Services, Inc. ANV Coalition, Inc. Beazley Berkley Technology Underwriters (a Berkley Company) Erin Eisenrich Brian Zimmer Michael South David Beabout Cory Simpson Maj Gen Matteo Martemucci, USAF Heather McMahon TJ White Nick Andersen Sean Plankey George A. Guillermo Christensen Hala Nelson Dan Van Wagenen Kurt Sanger David Eapen Andria Adigwe, CIPP/US Tiffany Yeung Jillian Cash Jacqueline Jonczyk, CIPP/US Kellen Carleton Harry Valetk Crum & Forster VeridatAI, Inc.

If there’s one thing missing in people’s understanding of the risks of agentic AI, it’s that there are *so many points of failure.* First, you have failures in the LLM that constitutes the core of the agent. Hallucinations, bias, privacy violations, etc. Second, you have all the tools to which it’s connected. In principle, this can be dozens of tools, including databases, enterprise software, other AIs, the internet, and more. So when an agent creates an output (or even more consequentially, makes a decision), it’s doing so based on SO MANY INTERDEPENDENCIES. The complexity increases exponentially. With all those nodes of interaction, it’s easy for things to go sideways. What’s more, since you can’t predict all the ways the agent will act prior to deployment, you’re necessarily sending the agent out without having tested it in all the contexts in which it will operate. If you can’t predict how something will act once deployed, you’ve got to keep a very close watch on how it behaves once you deploy. The importance of monitoring can’t be exaggerated when it comes to agentic AI. #ai #aiethics