How to Identify AI Vulnerabilities

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

Agentic AI Security: Risks We Can’t Ignore As agentic AI systems move from experimentation to real-world deployment, their attack surface expands rapidly. The visual highlights some of the most critical security vulnerabilities emerging in agent-based AI architectures—and why teams need to address them early. Key vulnerabilities to watch closely 🥷Token / Credential Theft – Secrets leaking through logs or configuration files remain one of the easiest attack vectors. 🕵️♂️Token Passthrough – Forwarding client tokens to backends without validation can cascade a single breach across systems. 🪢Rug Pull Attacks – Trusted maintainers or updates becoming malicious pose a serious supply-chain risk. 💉Prompt Injection – Hidden instructions that LLMs follow too readily; often trivial to exploit with critical impact. 🧪Tool Poisoning – Malicious commands embedded invisibly within tools or workflows. 💻Command Injection – Unfiltered inputs allowing attackers to execute arbitrary commands. ⛔️Unauthenticated Access – Optional or skipped authentication that exposes entire endpoints. The pattern is clear Most of these vulnerabilities are easy or trivial to exploit, yet their impact ranges from high to critical. Agentic AI doesn’t just generate content—it takes actions. That dramatically raises the cost of security failures. What this means for builders and leaders Treat AI agents as production-grade systems, not experiments ✔️Enforce strong authentication, token hygiene, and isolation ✔️Assume prompts, tools, and updates can be adversarial ✔️Build guardrails before increasing autonomy and scale Agentic AI is powerful, but without security-first design, it can quickly become a liability. How is your team approaching agentic AI security? #AgenticAI #AISecurity #CyberSecurity #LLM

Most teams say they’re testing their AI systems. But they’re only testing the easy stuff. They check accuracy. Maybe run a few prompt injection tests. Sometimes try to jailbreak the model. And if it holds up…they assume things are fine. But real AI pentesting goes much deeper. Attackers aren’t just asking clever prompts. They’re probing the entire system around the model. The prompts. The data. The tools. The infrastructure. Even the agent behavior. That’s why AI red teams think in attack categories, not just prompts. Here are some of the main areas they test: 1️⃣ Manipulation attacks Prompt injection, jailbreaks, or tricks that push the model to ignore rules. 2️⃣ Extraction attacks Pulling out hidden system prompts, training data, or sensitive knowledge. 3️⃣ Evasion attacks Adversarial inputs designed to slip past filters and guardrails. 4️⃣ Poisoning attacks Corrupting training data or knowledge bases so the model learns the wrong things. 5️⃣ Agentic attacks Abusing tools, memory, or multi-agent workflows to trigger unintended actions. 6️⃣ Infrastructure attacks Going after the system itself: denial-of-service, model theft, or insecure outputs. 7️⃣ Trust & safety testing Probing bias, safety boundaries, and behavioral consistency. The big takeaway: Testing an AI model isn’t the same as testing an AI system. Most vulnerabilities don’t live in the model alone. They show up in the connections between everything around it. So here’s a good question to ask your team: When you say you’ve “tested your AI”… are you testing the model, or the entire system? Because attackers will test all of it.

Imagine receiving what looks like a routine business email. You never even open it. Within minutes, your organisation’s most sensitive data is being silently transmitted to attackers. This isn’t science fiction. It happened with EchoLeak. AIM Security’s research team discovered the first zero-click AI vulnerability, targeting Microsoft 365 Copilot. The attack is elegant and terrifying: a single malicious email can trick Copilot into automatically exfiltrating email histories, SharePoint documents, Teams conversations, and calendar data. No user interaction required. No suspicious links to click. The AI agent does all the work for the attacker. Here’s what caught my attention as a security professional: The researchers bypassed Microsoft’s security filters using conversational prompt injection – disguising malicious instructions as normal business communications. They exploited markdown formatting quirks that Microsoft’s filters missed. Then they used browser behaviour to automatically trigger data theft when Copilot generated responses. Microsoft took five months to patch this (CVE-2025-32711). That timeline tells you everything about how deep this architectural flaw runs. The broader implication: this isn’t a Microsoft problem, it’s an AI ecosystem problem. Any AI agent that processes untrusted inputs alongside internal data faces similar risks. For Australian enterprises racing to deploy AI tools, EchoLeak exposes a critical blind spot. We’re securing the AI like it’s traditional software, but AI agents require fundamentally different security approaches. The researchers call it “LLM Scope Violation” – when AI systems can’t distinguish between trusted instructions and untrusted data. It’s a new vulnerability class that existing frameworks don’t adequately address. Three immediate actions for security leaders: • Implement granular access controls for AI systems • Deploy advanced prompt injection detection beyond keyword blocking • Consider excluding external communications from AI data retrieval EchoLeak proves that theoretical AI risks have materialised into practical attack vectors. The question isn’t whether similar vulnerabilities exist in other platforms – it’s when they’ll be discovered. #AISecurity #CyberSecurity #Microsoft365 #EnterpriseAI #InfoSec #Australia #TechLeadership https://lnkd.in/gNfxV3Nk

“Why is AI making some security teams more vulnerable? The answer has nothing to do with code.” Last year, a client asked me to “infuse AI” into their threat detection. Within weeks, alerts tripled—but so did burnout. Analysts grew numb to the noise, missing a real breach buried in automated false positives. The irony? Their shiny AI tool worked perfectly. AI isn’t a cybersecurity savior—it’s a force multiplier for human bias. -> Trained on historical data? It inherits past blindspots (like ignoring novel attack patterns). -> Tuned for speed? It prioritizes loud threats over subtle ones (think ransomware over data exfiltration). The most advanced SOCs now treat AI like a scalpel, not a sledgehammer: augmenting intuition, not replacing it. Gartner’s 2024 report claims 73% of breaches involved AI-driven tools. Dig deeper, and you’ll find 89% of those failures traced back to misconfigured human workflows—not model accuracy. Example: A Fortune 500 firm blocked 100% of phishing emails… while attackers pivoted to API exploits the AI never monitored. Before deploying any AI security tool, ask: “What will my team stop paying attention to?” Then: 1. Map its alerts to your actual risk profile (not vendor hype). 2. Reserve AI for repetitive tasks (log analysis) vs. high-stakes decisions (incident response). 3. Force a weekly “false positive audit” to retrain both models and analysts. AI won’t hack itself. The real vulnerability sits between the keyboard and the chair—but that’s fixable.

How secure is your AI? Adversarial attacks are exposing a critical vulnerability in AI systems—and the implications are massive. Let me explain. Adversarial attacks manipulate AI inputs, tricking models into making incorrect predictions. Think: self-driving cars misreading stop signs or facial recognition systems failing due to subtle pixel alterations. Here’s the reality: → Data Poisoning: Attackers inject malicious data during training, degrading the AI’s reliability. → Evasion Attacks: Inputs are modified at inference time, bypassing detection without altering the model. → Eroded Trust: As public awareness of these vulnerabilities grows, confidence in AI systems weakens. So, what’s the solution? ✔️ Adversarial Training: Exposing AI models to manipulated inputs during training strengthens their defenses. ✔️ Robust Data Management: Regular audits and sanitized training datasets reduce the risk of data poisoning. ✔️ Continuous Monitoring: Watching for unusual behavior can catch attacks in real time. The takeaway? AI security is no longer optional—it’s essential for maintaining trust, reliability, and innovation. As AI adoption grows, organizations must stay ahead of adversaries with proactive strategies and continuous improvement. How is your organization addressing the rising threat of adversarial attacks? Let’s discuss.

On July 26, the Department of Commerce, through the National Institute of Standards and Technology (NIST), has for the first time publicly released draft guidance from the U.S. AI Safety Institute, together with a testing platform designed to help AI system users and developers measure how certain types of attacks can degrade the performance of an AI system. * * * The new draft guidance published last Friday is called "Managing Misuse Risk for Dual-Use Foundation Models (NIST AI 800-1)" and intended to help software developers mitigate the risks stemming from generative AI and dual-use foundation models. It specifically targets the management of risks associated with the potential misuse of these models to cause harm. Misuse scenarios include the development of weapons of mass destruction, enabling cyber attacks, aiding deception, and generating harmful content such as child sexual abuse material (CSAM) and non-consensual intimate imagery (NCII). The draft guidance offers 7 key approaches for mitigating the risks that models will be misused, along with recommendations for how to implement them and how to be transparent about their implementation. NIST is accepting comments from the public on the draft until Sept. 9, 2024. Comments can be submitted to NISTAI800-1@nist.gov. * * * The test software platform "Dioptra" was released alongside the new draft guidance. It shall assist AI system users and developers in measuring how different types of attacks can impact the performance of AI systems and to identify vulnerabilities and enhance system security. The core vulnerability of an AI system lies in its model, which learns from large amounts of training data. However, if this data is tampered with or poisoned, the model might make incorrect decisions, such as misidentifying road signs, leading to potentially dangerous outcomes. Dioptra has been developed to test machine learning models against adversarial attacks and to measure the impact of these attacks on system performance. GitHub link: https://lnkd.in/gy84pbRc * * * Released Earlier: 1.) Two Guidance Documents: The AI RMF Generative AI Profile (NIST AI 600-1) and the Secure Software Development Practices for Generative AI and Dual-Use Foundation Models (NIST Special Publication (SP) 800-218A) were initially released in draft form on April 29 for public comment. These documents serve as companion resources to NIST’s AI Risk Management Framework (AI RMF) and Secure Software Development Framework (SSDF). They now have been finalized. 2.) A Plan for Global Engagement on AI Standards (NIST AI 100-5): This publication, also initially released in draft form on April 29, proposes a strategy for U.S. stakeholders to collaborate internationally on AI standards. Its final version was released along with the two guidance documents. Link to NIST PR: https://lnkd.in/gZCeHHqY #AIsafety

Adversaries are watching. Are you ready? Azure OpenAI from an Attacker's Perspective. As defenders strengthen their cloud defenses, adversaries analyze the same architectures to find gaps to exploit. Let’s take a quick look at Azure OpenAI Service—a goldmine for both innovation and potential missteps. What Stands Out for an Attacker? 1️⃣ Data Residency & Isolation: While data remains customer controlled and maybe double encrypted, attackers might target storage misconfigurations in the Assistants / Batch services, where prompts and completions reside temporarily. Weak RBAC configurations could expose sensitive files and logs stored in these areas. 2️⃣ Sandboxed Code Interpreter: The isolated environment ensures secure code execution, but attackers might attempt to exploit vulnerabilities in sandbox boundaries or inject malicious payloads to gain access to sensitive data during runtime. 3️⃣ Asynchronous Abuse Monitoring: It is a critical component for detecting misuse but also a potential data-retention bottleneck. Attackers may target monitoring APIs or exploit the X day retention to obscure their tracks or hijack historical prompts for sensitive insights. 4️⃣ Fine Tuning Workflows: Customers love the exclusivity of fine-tuned models, but attackers could leverage phishing attacks to hijack API keys or access fine-tuning data that resides in storage. Compromising a fine-tuned model could reveal proprietary insights or customer IP. 5️⃣ Batch API Vulnerabilities: With batch processing in preview, this could be a point of weakness for bulk data manipulation attacks or injection-based techniques. Monitoring batch jobs for anomalies is crucial. As enterprises adopt Azure OpenAI Service to supercharge their operations, it is critical to stay ahead of evolving attacker techniques. Every layer of this architecture—from encrypted storage to sandboxed environments—presents opportunities and challenges. For defenders, understanding these risks is the first step in hardening the fortress. #security #artificialintelligence #cloudsecurity

*The Autonomous Cyber Defence Trinity: Moving from Reactive Defence to Predictive Resilience.* 1. AI GRC (Governance, Risk, and Compliance) Focus: Transitioning from "Point-in-Time" to "Continuous" oversight. The Problem: Reliance on spreadsheets, manual audits, and outdated policies. The AI Solution: - Automated Policy Mapping: AI reads new regulations (like the EU AI Act or updated NIST frameworks) and maps them to your controls instantly. - Predictive Risk Scoring: Utilises internal data to predict which business units are most likely to face a breach. - Dynamic Compliance: Real-time dashboards provide a 24/7 view of compliance posture, not just during audit season. Visual Cue: An automated "Radar" or "Shield" icon representing constant monitoring. 2. AI Pentesting (Penetration Testing) Focus: Evolving from "Annual Scans" to "Continuous Adversarial Testing." The Problem: Traditional pentests are costly, slow, and only capture a single moment in time. The AI Solution: - Automated Exploit Simulation: AI "agents" emulate hacker behavior to uncover complex attack paths that static scanners overlook. - Vulnerability Prioritisation: Rather than presenting a list of 1,000 "Criticals," AI identifies which vulnerabilities are actually reachable and exploitable. - Red Teaming at Scale: Conducting thousands of simulated attacks simultaneously without the need for a large human team. Visual Cue: A "Sword" or "Hacker-bot" icon representing active, offensive testing. 3. AI SOC (Security Operations Centre) Focus: Shifting from "Alert Fatigue" to "Automated Remediation." The Problem: Analysts face overwhelming "noise" from false positives and slow response times. The AI Solution: - Noise Reduction: AI filters out 95% of false positives, emphasising only the "Signal." - Autonomous Response #CyberSecurity #ArtificialIntelligence #AI #InformationSecurity #SecurityLeadership #AIGovernance #RiskManagement #Compliance #PenetrationTesting #SOC #CISO #CyberRisk #EnterpriseSecurity #DigitalTrust