## **Agentic AI - Threats and Mitigations (OWASP, February 2025)** ![[Owasp_Agentic AI Threats and Mitigations.pdf]] This comprehensive document by the OWASP Agentic Security Initiative outlines the security risks and mitigations associated with agentic AI - autonomous systems enabled by large language models (LLMs) and generative AI. As these systems gain complexity and autonomy, new and evolving threat vectors arise. The document introduces a structured threat model, taxonomy, and playbooks aimed at builders and defenders of agentic applications, ranging from developers and security engineers to architects and decision-makers. It builds on existing OWASP frameworks while addressing uniquely agentic risks. **Key Insights** - **Agentic AI Fundamentals**: Agentic AI systems, built using LLMs, exhibit planning, reasoning, memory retention, and tool usage to autonomously achieve goals. Architectures vary from single-agent to multi-agent systems with complex interactions and potential for decentralized decision-making. - **Reference Architecture**: These systems typically include components like embedded agentic apps, LLMs for reasoning, tool interfaces, external APIs, and memory services (short- and long-term). Multi-agent systems introduce inter-agent communication and coordination, increasing attack surfaces. - **Threat Modeling Framework**: The threat model identifies both novel and agentic variants of traditional risks. It avoids strict methodologies in favor of a layered reference architecture that maps capabilities to threats. - **Agentic Threat Taxonomy**: Fifteen core threats are identified, including: - **Memory Poisoning** - Attackers corrupt an agent's memory to alter decision-making or inject malicious data. - **Tool Misuse** - Agents are tricked into misusing their integrated tools to perform unauthorized actions. - **Privilege Compromise** - Exploiting weak permission systems to gain unauthorized access or escalate privileges. - **Resource Overload** - Overwhelming the agent with tasks or inputs to degrade performance or cause failure. - **Cascading Hallucination Attacks** - False information compounds through memory or communication, spreading systemic errors. - **Intent Breaking & Goal Manipulation** - Attackers alter the agent's goals or planning to steer it toward harmful actions. - **Misaligned & Deceptive Behaviors** - Agents take harmful actions while appearing compliant, due to flawed reasoning or goal pursuit. - **Repudiation & Untraceability** - Lack of logs or traceability makes it impossible to audit or investigate agent actions. - **Identity Spoofing & Impersonation** - Attackers impersonate agents or users to perform unauthorized operations undetected. - **Overwhelming Human in the Loop (HITL)** - Attacks flood human reviewers with requests, leading to fatigue and missed threats. - **Unexpected RCE and Code Attacks** - AI-generated code is exploited to run malicious scripts or gain system control. - **Agent Communication Poisoning** - Tampering with inter-agent communication to spread misinformation or disrupt workflows. - **Rogue Agents in Multi-Agent Systems** - Compromised agents act independently to execute unauthorized actions or hide malicious behavior. - **Human Attacks on Multi-Agent Systems** - Exploiting agent delegation and trust to escalate privileges or disrupt systems. - **Playbooks**: Six mitigation playbooks are offered, addressing agent reasoning manipulation, memory integrity, tool misuse, identity control, HITL vulnerabilities, and multi-agent coordination threats. Playbooks: - **Preventing AI Agent Reasoning Manipulation** - Controls to stop attackers from altering agent goals or logic, and ensures traceable decision-making. - **Preventing Memory Poisoning & AI Knowledge Corruption** - Secures memory access, validates knowledge sources, and blocks spread of manipulated or false data. - **Securing AI Tool Execution & Preventing Unauthorized Actions** - Restricts tool use, monitors executions, and prevents privilege escalation or malicious code execution. - **Strengthening Authentication, Identity & Privilege Controls** - Enhances identity validation, enforces strict access controls, and detects spoofing or impersonation. - **Protecting HITL & Preventing Decision Fatigue Exploits** - Reduces cognitive overload for human reviewers, prioritizes high-risk actions, and prevents manipulation. - **Securing Multi-Agent Communication & Trust Mechanisms** - Protects inter-agent communication, detects rogue agents, and enforces trust and consensus protocols. **Actionable Takeaways** - **Design with Role Segregation**: Architect agents with granular roles, restrict privilege escalation, and use just-in-time access to tools and APIs. - **Implement Goal Consistency Validation**: Track and flag behavioral shifts in agents to catch goal manipulation early. - **Secure Memory and Logging**: Segment agent memory, validate inputs before storage, and maintain cryptographically signed logs to ensure traceability and enable forensics. - **Tool Invocation Governance**: Enforce strict boundaries for tool use, validate execution chains, and monitor for anomalous command behavior. - **Protect Against Overload and Manipulation**: Establish thresholds to detect HITL fatigue, rate-limit agent operations, and ensure fallback mechanisms in multi-agent systems. - **Authenticate Everything**: Use MFA for agents, ensure agent-to-agent verification, and restrict credential persistence. - **Monitor and Detect**: Deploy real-time anomaly detection for agent behavior, memory modification frequency, tool usage, and inter-agent communications. - **Build Resilience**: Integrate behavioral profiling, deception detection strategies, and multi-agent consensus mechanisms for high-trust decisions. This document is a foundational resource for securing the next generation of AI agents, offering an urgently needed threat lens and practical strategies for defending against evolving AI-enabled attack surfaces.