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

Most AI security programs protect the wrong thing 🛡️ Traditional cybersecurity is built around the network perimeter, keeping attackers out, protecting the data inside, detecting intrusions when they happen. AI systems introduce a different attack surface. The model itself is the target. The training data is the target. The inference pipeline is the target. Let's look at the three attack categories every GRC and security team needs to understand now. 👇 1️⃣ Data Poisoning: An adversary introduces manipulated data into the training set, causing the model to learn incorrect patterns or develop hidden behaviors that activate under specific conditions. The most dangerous variant is the backdoor attack, in which the model performs normally on clean inputs and passes every standard accuracy test, then fails in predictable, attacker-controlled ways when triggered by a specific input pattern. The governance failure mode is subtle. Poisoned models look fine in testing. The gap between "model passed evaluation" and "model is safe to deploy" is exactly where data governance lives. 2️⃣ Prompt Injection: The defining security threat of LLM deployment. An attacker embeds malicious instructions in content the model processes, a user message, a retrieved document, a webpage, that override the model's intended behavior. Indirect injection is the more dangerous variant. The model retrieves attacker-controlled content during operation, redirecting its actions without the user or operator knowing. 💡 Agentic AI systems are particularly exposed. A model that can take actions, send emails, query databases, or execute code is one where a successful prompt injection becomes an execution vector, not just an output problem. 3️⃣ Model Extraction: An attacker queries a deployed model repeatedly, observing inputs and outputs, and uses those observations to reconstruct a functional replica. The replica can compete commercially, enable adversarial attacks offline, or reveal vulnerabilities exploitable against the original. This is an intellectual property and security risk simultaneously. The attack is difficult to detect because it looks like normal API usage. What makes these different from traditional cybersecurity risks is that they target the AI system's behavior and integrity, not just surrounding infrastructure. A firewall doesn't stop a poisoned training set. Endpoint detection doesn't catch prompt injection in a retrieved document. Organizations need AI-specific threat modeling, not traditional controls applied to AI deployments. MITRE ATLAS maps these attacks in detail. OWASP's LLM Top 10 is a good starting list: https://lnkd.in/g3ZRuZNq Drop a comment and let me know which of these three attack categories you need more to learn more about! #AIGovernance #AIRisk #Cybersecurity #GRC #AI

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

���� Agentic AI is powerful… but it’s also expanding your attack surface. Most teams are rushing to build AI agents. Very few are thinking deeply about securing them. That’s a problem. Because vulnerabilities in Agentic AI aren’t theoretical, they’re already exploitable. Here are 7 critical risks every builder should understand: 🔐 Token / Credential Theft Sensitive data exposed via logs or insecure storage. → Easy to exploit. High impact. 🔁 Token Passthrough Forwarding tokens without validation = open door for abuse. → Attackers love this. 💉 Prompt Injection Malicious instructions hidden in inputs. → LLMs will follow them if unchecked. ⚙️ Command Injection Unfiltered inputs triggering unintended system actions. → Critical severity. Often overlooked. 🧪 Tool Poisoning Tampered tools executing hidden malicious logic. → Trust = vulnerability. 🚫 Unauthenticated Access Endpoints without proper auth. → Shockingly common. 💣 Rug Pull Attacks Compromised maintainers pushing malicious updates. → Supply chain risk is real. The takeaway? If your AI agent can: • Access tools • Execute commands • Use credentials • Interact with external systems 👉 Then it must be treated like production infrastructure, not a prototype. 🔧 What you should do next: • Validate every input • Implement strict auth & access control • Sanitize tool usage • Monitor logs (securely!) • Assume adversarial behavior AI doesn’t just introduce new capabilities. It introduces new threat models. And the teams that win will be the ones who build secure AI by design. 💬 Curious, which of these risks are you actively addressing today?

Prompt Hacking. Jailbreaks. Injection Attacks. If your team uses AI, this is your new attack Surface. You learned how to protect your network. Train your staff on phishing. Lock down your data. But nobody sat you down and said, "Here's how attackers are manipulating the AI tools you already use." So let's fix that. What is Prompt Hacking? Attackers manipulate inputs to AI systems so they: ↳ Reveal sensitive information ↳ Bypass security restrictions ↳ Execute unintended instructions Unlike traditional cyberattacks, this exploits the AI's decision-making logic, not software vulnerabilities. How These Attacks Work: Direct Injection ↳ Attackers manipulate prompts to extract data or trigger unauthorized actions. Indirect Injection ↳ Malicious instructions hidden in documents or URLs the AI processes. Stored Injection ↳ Malicious prompts saved and executed later when retrieved. Jailbreaks ↳ Exploits content filter weaknesses to generate restricted outputs. Prompt Leaking ↳ AI tricked into revealing its own system instructions. Real Incidents That Already Happened: ShadowLeak (2025) ↳ Zero-click vulnerability in ChatGPT's Deep Research agent. ↳ Sensitive data extracted from OpenAI servers without user interaction. MalTerminal ↳ GPT-4 used to generate ransomware scripts and reverse shells. Google Calendar Exploit ↳ Malicious calendar invites hijacked ChatGPT's Gmail connector. How to Protect Your AI Systems: ↳ Filter and sanitize inputs before they reach the AI. ↳ Limit who can submit prompts affecting critical operations. ↳ Monitor outputs for unusual behavior in real time. ↳ Run adversarial tests to find vulnerabilities before attackers do. ↳ Add human review for high-risk AI decisions. ↳ Keep AI systems patched and updated. Your AI tools are powerful. But they trust user inputs by default. That trust is exactly what attackers exploit. The organizations that stay safe aren't avoiding AI. They're securing it. You've got this. ♻️ Repost to help a business owner understand the new AI attack surface. ➕ Follow Leslie Babel for more on making technology simple and secure.

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

If you're a software engineer working with AI in your workflow, here's a simple prompt to make sure you're 100% covered from a security point of view, based on my last 6 years in DevSecOps: Paste this into your agent before you ship anything important: You are a senior security engineer performing an adversarial security audit of this codebase, app, or system design. Assume it will run in a hostile environment with motivated attackers. Audit these layers: - frontend - backend - auth and permissions - database and storage - infrastructure and deployment - third-party integrations and dependencies Your job: 1. Find critical, high, medium, and low severity issues 2. Catch logic flaws, not just common patterns 3. Identify multi-step attack paths 4. Flag unusual or non-obvious risks 5. Think like a creative attacker, not a checklist scanner Threat model first: - define attacker types - identify entry points - identify trust boundaries - identify sensitive assets like data, secrets, tokens, and permissions Check for issues in: - auth, sessions, password reset, token misuse - broken authorization, IDOR, privilege escalation - SQL, NoSQL, command, template, and file upload attacks - XSS, CSRF, replay, race conditions, cache poisoning - mass assignment, rate limit gaps, brute force paths - secret leaks, weak crypto, insecure storage, bad logging - CORS, CSP, headers, debug endpoints, env leaks - cloud or deployment misconfigurations - vulnerable or risky dependencies Also try to discover: - feature abuse - impossible-but-possible behavior - state desync issues - weak trust assumptions - attack chains built from smaller issues Output format: 1. Vulnerability summary by severity 2. Detailed findings with: - title - severity - affected component - description - exploitation steps - impact - recommended fix 3. Attack chains 4. Secure design improvements Important: - assume nothing is safe - infer risk where context is missing - be exhaustive - if something looks risky but uncertain, flag it and explain why Most people use AI to write code faster. Very few use it to pressure test what they just built. That second use case will save you a lot more pain. -- 📢 Follow saed if you enjoyed this post 🔖 Be sure to subscribe to the newsletter: https://lnkd.in/eD7hgbnk 📹 Reach me on https://lnkd.in/eZ9mU5Ka for open DM's

“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.