Understanding Agentic AI Threat Modeling

This new guide from the OWASP® Foundation Agentic Security Initiative for developers, architects, security professionals, and platform engineers building or securing agentic AI applications, published Feb 17, 2025, provides a threat-model-based reference for understanding emerging agentic AI threats and their mitigations. Link: https://lnkd.in/gFVHb2BF * * * The OWASP Agentic AI Threat Model highlights 15 major threats in AI-driven agents and potential mitigations: 1️⃣ Memory Poisoning – Prevent unauthorized data manipulation via session isolation & anomaly detection. 2️⃣ Tool Misuse – Enforce strict tool access controls & execution monitoring to prevent unauthorized actions. 3️⃣ Privilege Compromise – Use granular permission controls & role validation to prevent privilege escalation. 4️⃣ Resource Overload – Implement rate limiting & adaptive scaling to mitigate system failures. 5️⃣ Cascading Hallucinations – Deploy multi-source validation & output monitoring to reduce misinformation spread. 6️⃣ Intent Breaking & Goal Manipulation – Use goal alignment audits & AI behavioral tracking to prevent agent deviation. 7️⃣ Misaligned & Deceptive Behaviors – Require human confirmation & deception detection for high-risk AI decisions. 8️⃣ Repudiation & Untraceability – Ensure cryptographic logging & real-time monitoring for accountability. 9️⃣ Identity Spoofing & Impersonation – Strengthen identity validation & trust boundaries to prevent fraud. 🔟 Overwhelming Human Oversight – Introduce adaptive AI-human interaction thresholds to prevent decision fatigue. 1️⃣1️⃣ Unexpected Code Execution (RCE) – Sandbox execution & monitor AI-generated scripts for unauthorized actions. 1️⃣2️⃣ Agent Communication Poisoning – Secure agent-to-agent interactions with cryptographic authentication. 1️⃣3️⃣ Rogue Agents in Multi-Agent Systems – Monitor for unauthorized agent activities & enforce policy constraints. 1️⃣4️⃣ Human Attacks on Multi-Agent Systems – Restrict agent delegation & enforce inter-agent authentication. 1️⃣5️⃣ Human Manipulation – Implement response validation & content filtering to detect manipulated AI outputs. * * * The Agentic Threats Taxonomy Navigator then provides a structured approach to identifying and assessing agentic AI security risks by leading though 6 questions: 1️⃣ Autonomy & Reasoning Risks – Does the AI autonomously decide steps to achieve goals? 2️⃣ Memory-Based Threats – Does the AI rely on stored memory for decision-making? 3️⃣ Tool & Execution Threats – Does the AI use tools, system commands, or external integrations? 4️⃣ Authentication & Spoofing Risks – Does AI require authentication for users, tools, or services? 5️⃣ Human-In-The-Loop (HITL) Exploits – Does AI require human engagement for decisions? 6️⃣ Multi-Agent System Risks – Does the AI system rely on multiple interacting agents?

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

We built HTTPS with a threat model. We built MCP with a demo. Researchers just published the first formal security threat model covering four major AI agent protocols. MCP. A2A. Agora. ANP. All of them. They found twelve protocol-level risks. No standardized threat modeling framework existed before this paper. Let that sink in. Billions in venture funding. Thousands of production deployments. Zero formal adversary models. Some of the findings are brutal. A2A tokens are coarse-grained by default. A token scoped for a single payment can access unrelated resources. That violates least privilege on day one. MCP community installers distribute packages without signature verification. Attackers can publish altered installers that deploy malware through trusted channels. Every protocol launched with a focus on interoperability and speed. Security was left as an exercise for the implementer. What's worse? The tools to prevent this already existed. WHEN WILL THE MARKET LEARN?!?!?!?!?! CSA published the MAESTRO framework in February 2025, but early iterations date back to 2024. Seven layers of structured threat modeling built specifically for agentic AI systems. Ken Huang already applied it to Google's A2A protocol and OpenAI's Responses API. It covers agent ecosystem trust, cross-layer attack paths, and protocol-level interactions. The OWASP Agentic Security Initiative has been mapping these exact risks for over a year. The Top 10 for Agentic Applications covers excessive agency, tool misuse, cascading failures, and trust boundary violations. Two independent frameworks. Both were available before these protocols reached production. Both were ignored by the teams shipping them. The Anbiaee paper didn't discover new risk categories. It independently validated what MAESTRO and OWASP already documented. That's not a research breakthrough. That's an indictment. TLS started with a threat model. OAuth 2.0 went through years of adversarial review. HTTPS required certificate authorities and trust chains from the beginning. Agent protocols did the opposite. Ship the spec. Get adoption. Retrofit security later. Even when purpose-built security frameworks were sitting right there. If you're building on MCP or A2A today, the question isn't whether threat models exist for your protocol. They do. MAESTRO and OWASP both have you covered. The question is whether you've actually used one before shipping. 👉 Follow and connect for more AI and cybersecurity insights with the occasional rant #AgenticAISecurity #OWASPTop10

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗵𝗲𝗿𝗲. 𝗦𝗼 𝗮𝗿𝗲 𝘁𝗵𝗲 𝘁𝗵𝗿𝗲𝗮𝘁𝘀. AI agents are no longer just conceptual — they’re deployed, autonomous, and integrated into real-world applications. But as Palo Alto Networks rightly warns: the moment agents become tool-empowered, they become threat-prone. 𝗝𝗮𝘄-𝗱𝗿𝗼𝗽𝗽𝗶𝗻𝗴 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:- • Prompt injection can hijack an agent without jailbreaks — unsecured instructions are enough. • Code interpreters open doors to credential theft, SQL injection, and cloud token exfiltration. • Agent-to-agent communication is poisonable — collaborative workflows can be manipulated. • These flaws are framework-agnostic — the issue lies in design, not the tool. 𝗧𝗵𝗲 𝗯𝗶𝗴 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆? Agentic AI needs defense-in-depth:- • Prompt hardening • Input validation • Tool sandboxing • Runtime monitoring AI safety isn’t just a philosophical debate anymore — it’s a cybersecurity and systems engineering imperative. 🔐 Let’s raise the guardrails before attackers raise the stakes. #AgenticAI #AISecurity #PromptInjection #AIGovernance #GenAI #LLMsecurity #CyberSecurity #AI4Good #AIrisks #AIethics #ResponsibleAI #LLMs #AutoGen #CrewAI #PaloAltoNetworks

Agentic AI is rapidly transforming industries, combining large language model (#LLM) outputs with reasoning and autonomous actions to perform complex, multi-step tasks. This technological shift promises immense economic potential, impacting sectors from software to services. However, this powerful new capability introduces a fundamentally new threat surface and significant risks. The "State of Agentic AI Security and Governance" report, a critical resource from the OWASP GenAI Security Project's Agentic Security Initiative, provides crucial insights into navigating this evolving landscape. Key Challenges & Risks highlighted: • Probabilistic Nature: Agentic AI is inherently non-deterministic, making outputs and decisions variable, and thus, risk analysis and reproducibility are challenging. • Expanded Threat Surface: Agents are vulnerable to memory poisoning, tool misuse, prompt injection, and amplified insider threats due to their privileged access to systems and data. • Regulatory Lag: Current regulations often lag behind the rapid development of agentic approaches, leading to increasing compliance complexity. • Multi-Agent Complexity: Risks like adversarial coordination, toolchain vulnerabilities, and deceptive social engineering are amplified in multi-agent architectures. Addressing these challenges requires a paradigm shift: • Proactive Security: Transition from traditional controls to a proactive, embedded, defense-in-depth approach across the entire agent lifecycle (development, testing, runtime). • Key Technical Safeguards: Implement fine-grained access control, runtime monitoring of inputs/outputs and actions, memory and session state hygiene, and secure tool integration and permissioning. • Dynamic Governance: Governance must evolve toward dynamic, real-time oversight that continuously monitors agent behavior, automates compliance, and enforces explainability and accountability. • Anticipated Regulatory Convergence: Global regulators are moving towards continuous compliance requirements and stricter human-in-the-loop oversight, with frameworks like the EU AI Act, NIST AI RMF, and ISO/IEC 42001 offering initial guidance. This report is essential for builders and defenders of agentic applications, including developers, architects, security professionals, and decision-makers involved in building, procuring, or managing agentic systems. It emphasizes that now is the time to implement rigorous security and governance controls to keep pace with the evolving agentic landscape and ensure secure, responsible deployment. Stay informed and secure your Agentic AI initiatives! #AgenticAI #AIsecurity #AIGovernance #OWASP #GenAISecurity #Cybersecurity #LLMs #FutureOfAI

Today, Anthropic shared the first AI orchestrated cyber espionage attack. We are now living in a new world where you can no longer protect yourself with layers and layers of "cybersecurity tools" that are really just compliance checkmarks. In stead, you need to understand the implications of this attack - and why you need to protect your most important assets assuming the breach is coming. Anthropic’s report describes a state-aligned actor using AI agents to run almost the entire operation: reconnaissance, vulnerability analysis, exploitation, credential harvesting, lateral movement, data extraction, even summarizing what they found. This wasn’t “AI helping an attacker.” This was AI orchestrating the attack. And that changes the timeline. It changes the scale. It changes who can execute sophisticated operations. Most importantly, it changes what gets targeted. When attackers automate reconnaissance and lateral movement, your network, your endpoints, your “zero trust-ish” setup—none of it can guarantee containment. Credentials leak. SaaS sprawl grows. Agentic workflows run inside your company whether you planned for them or not. Which leaves one question: What happens to your sensitive data when the perimeter fails? If the answer is “it’s still in the clear, available to anything with valid credentials,” then you are operating with an outdated threat model. The shift happening now is simple but profound: Security has to move from protecting systems to protecting the flow of data itself. That means isolating sensitive data, minimizing where it lives, and applying real controls—encryption, tokenization, residency—before it moves into agents, models, logs, or pipelines. AI has changed the offense. Organizations now have to change the defense. Not by adding another tool to the pile, but by treating data itself as the boundary. This is where the next decade of security is headed.

Anthropic Just Documented the First AI-Orchestrated Cyber Espionage Campaign → 30 Targets → 80-90% Autonomous Operations GTG-1002 changed everything we thought we knew about AI agent security. Chinese state actors didn't just use Claude for advice. They turned it into an autonomous penetration testing orchestrator using MCP servers. Here's what your security team needs to understand... The Technical Reality ↳ Claude Code + Model Context Protocol = autonomous attack framework ↳ AI executed reconnaissance, exploitation, lateral movement, data exfiltration ↳ Humans only intervened at strategic decision gates (10-20% of operations) ↳ Peak activity: thousands of requests per second ↳ Multiple simultaneous intrusions across major tech companies and government agencies The Evolution from Vibe Coding to Autonomous Attacks In June 2025: "Vibe hacking" - humans directing operations November 2025: AI autonomously discovering vulnerabilities and exploiting them at scale What Teams Should Learn The Bypass Method: ↳ Role-play convinced Claude it was doing "defensive security testing" ↳ Social engineering the AI model itself ↳ Individual tasks appeared legitimate when evaluated in isolation The Infrastructure: ↳ MCP servers orchestrated commodity penetration testing tools ↳ No custom malware needed ↳ Integration over innovation Critical Limitation: ↳ AI hallucinations created false positives ↳ Claimed credentials that didn't work ↳ "Critical discoveries" turned out to be public information ↳ Full autonomy still requires human validation Security Implications for Founders The barriers to sophisticated cyberattacks dropped substantially. Less experienced groups can now potentially execute nation-state level operations. But here's what matters: The same AI capabilities enabling these attacks are critical for defense. SOC automation, threat detection, vulnerability assessment, incident response. Key Takeaways for Your Team ↳ Experiment with AI for defensive security operations ↳ Build detection systems for autonomous attack patterns ↳ Implement stronger safety controls and validation layers ↳ Assume AI-orchestrated attacks are now standard threat landscape ↳ Test your systems against AI-driven reconnaissance This isn't 2023 anymore. Your security posture needs to account for AI agents that can execute full attack chains with minimal human oversight. The question isn't whether AI will be used in cyberattacks. The question is whether your defenses account for AI-orchestrated operations happening right now. P.S. Building AI agents or implementing MCP in your infrastructure? Security-first architecture isn't optional anymore. One misconfigured agent with access to production systems = complete compromise.

You don't need to compromise the model to compromise the agent. A new paper, "Agentic AI as a Cybersecurity Attack Surface," makes a point folks such as Ken Huang and Rock Lambros among others have been hammering: the attack surface for AI agents isn't the model weights or the infrastructure, it's the context. Traditional software resolves dependencies at build time, libraries are declared, pinned, and verified before anything runs. Agentic systems? They assemble their execution context at runtime through what the authors call "Stochastic Dependency Resolution." Retrieved documents, external APIs, tool descriptions, these become implicit inference-time dependencies that directly shape reasoning and action. This means context is no longer passive input. It's an active component of the attack surface. The paper introduces the "Man-in-the-Environment" (MitE) adversary, an attacker who doesn't need access to your code, your model weights, or your infrastructure. They just need to manipulate the environmental artifacts your agent consumes: → Data Supply Chain: Injecting malicious content into context windows, memory banks, or external databases to alter agent perception → Tool Supply Chain: Subverting how agents discover, implement, and invoke tools → Viral Agent Loop: Poisoned outputs re-enter the environment as tainted context, enabling self-propagating compromise across agent interactions This is the fundamental paradigm shift the industry needs to internalize. We've spent decades building security models around protecting infrastructure, code integrity, and build pipelines. Agentic AI flips this entirely, the runtime environment IS the supply chain. As the authors put it, compromise can occur "without modifying source code, model weights, or infrastructure, purely through manipulation of the external environments that agents observe and interact with." If your agent security strategy starts and stops at guardrails around the model, you're defending the wrong perimeter. This is a great, short 9 page read on the attack surfaces of Agentic AI Paper -> https://lnkd.in/ek6J_dir

𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐢𝐬 𝐜𝐨𝐦𝐢𝐧𝐠 𝐟𝐚𝐬𝐭. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐫𝐢𝐬𝐤? 𝐈𝐭’𝐬 𝐢𝐧𝐬𝐞𝐜𝐮𝐫𝐞 𝐜𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐢𝐨𝐧. As LLMs evolve into autonomous agents capable of delegating tasks, invoking APIs, and collaborating with other agents, the architecture shifts. We’re no longer building models. We’re building distributed AI systems. And distributed systems demand trust boundaries, identity protocols, and secure coordination layers. A new paper offers one of the first serious treatments of Google’s A2A (Agent2Agent) protocol. It tackles the emerging problem of agent identity, task integrity, and inter-agent trust. Key takeaways: • Agent cards act as verifiable identity tokens for each agent • Task delegation must be traceable, with clear lineage and role boundaries • Authentication happens agent to agent, not just user to agent • The protocol works closely with the Model Context Protocol (MCP), enabling secure state sharing across execution chains The authors use the MAESTRO framework to run a threat model, and it’s clear we’re entering new territory: • Agents impersonating others in long chains of delegation • Sensitive context leaking between tasks and roles • Models exploiting ambiguities in open-ended requests Why this matters If you’re building agentic workflows for customer support, enterprise orchestration, or RPA-style automation, you’re going to hit this fast. The question won’t just be “Did the agent work?” It’ll be: • Who authorized it? • What was it allowed to see? • How was the output verified? • What context was shared, when, and with whom? The strategic lens • We need agent governance as a native part of the runtime, not a bolt-on audit log • Platform builders should treat A2A-like protocols as foundational, not optional • Enterprise buyers will soon ask vendors, “Do you support agent identity, delegation tracing, and zero trust agent networks?” This is where agent architecture meets enterprise-grade engineering. Ignore this layer and you’re not just exposing data. You’re creating systems where no one can confidently answer what happened, who triggered it, or why. We’ve moved beyond the sandbox. Time to build like it.

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