When agents call the shots: why your security team needs new playbooks
"Agentic AI systems demand more comprehensive evaluation because their planning, reasoning, tool utilization, and autonomous capabilities create attack surfaces and failure modes that extend far beyond those present in standard LLM or generative AI models."
Huang, Ken, et al. "Agentic AI Red Teaming Guide." Cloud Security Alliance AI Organizational Responsibilities Working Group, 2025.
I've spent the last week diving into the Cloud Security Alliance's latest red teaming guide for agentic AI, and frankly, it's changed how I think about our upcoming AI deployment strategy. The 62-page framework tackles something most of us are still figuring out: how do you actually test systems that plan, reason, and act autonomously when traditional security approaches assume you're dealing with predictable, deterministic software?
The document, led by Ken Huang with contributions from both CSA and OWASP AI Exchange teams, makes one thing crystal clear from the start. Agentic AI systems demand more comprehensive evaluation because their planning, reasoning, tool utilization, and autonomous capabilities create attack surfaces that extend far beyond those present in standard LLM or generative AI models. This isn't just another incremental security challenge—it's a fundamental shift in how we need to think about risk.
What caught my attention immediately was their breakdown of 12 threat categories, each with specific testing requirements that go well beyond prompt injection or model poisoning. Take "Agent Authorization and Control Hijacking," which the guide positions as a primary concern. The testing methodology here involves simulating unauthorized access attempts through APIs, verifying rejection of spoofed credentials, and evaluating responses to malformed commands. But it goes deeper—they want you testing whether agents properly relinquish temporary permissions upon task completion and whether the system prevents unintended permission escalation through task transitions.
The "Checker-Out-of-the-Loop" category hits particularly close to home for anyone dealing with autonomous systems in regulated environments. The guide emphasizes testing whether human or automated checkers remain actively informed during unsafe operations or threshold breaches. Their actionable steps include simulating API rate-limiting scenarios to evaluate if agents continue raising alerts reliably under degraded conditions, and testing multi-channel alert mechanisms to ensure delivery to checkers in different conditions like network downtime. For systems with real-time operating constraints, they specifically call out testing prevalence of lags due to hallucinations, monitoring, and evaluation time requirements.
Where this gets especially relevant for product and legal teams is the "Agent Critical System Interaction" section. The guide recognizes that many agentic systems will interact with physical infrastructure, IoT devices, and critical digital systems. Their testing approach involves simulating unsafe inputs, testing communication security with IoT devices, and evaluating failsafe mechanisms. They specifically mention testing for real-time behavior enforcement and measuring communication lags among components. This matters because when an agent controls physical systems, the blast radius of a security failure extends beyond data breaches into operational safety.
The supply chain considerations are sobering. The guide dedicates an entire section to "Agent Supply Chain and Dependency Attacks," acknowledging that agents rely heavily on external libraries, plugins, and APIs that could be exploited to alter functionality. They reference recent research on MCP Security Notification Tool Poisoning Attacks and emphasize testing agent resilience against integrating malicious tools. Their methodology includes introducing tampered dependencies, simulating compromised services, and testing deployment pipeline security. What's particularly useful is their focus on dynamic verification of third-party libraries and API services, including cryptographic checks where applicable.
Memory and context manipulation represents another area where traditional security thinking falls short. The guide's approach to "Agent Memory and Context Manipulation" testing involves simulating scenarios where context is reset or lost to observe if critical operational constraints are forgotten. They want you testing cross-session and cross-application data leakage, memory poisoning scenarios, and temporal attacks that exploit limited memory windows. Their emphasis on session isolation and secure memory management reflects the reality that agents often maintain state across interactions in ways that traditional applications don't.
Perhaps most importantly for risk management, the "Agent Impact Chain and Blast Radius" section addresses cascading failures. The testing methodology involves simulating compromise of a single agent and tracking propagation effects, testing trust relationships between agents, and evaluating containment mechanisms. They specifically call out testing failover mechanisms and assessing whether critical processes can continue operating despite an agent compromise. This directly informs how we should think about network segmentation and permission compartmentalization for agent deployments.
The guide's treatment of multi-agent exploitation scenarios is particularly thorough. Beyond basic communication security, they want you testing trust relationship abuse, coordination protocol manipulation, and what they call "confused deputy" attacks where privileged agents are exploited to perform unauthorized actions. Their approach includes testing whether agents validate all requests against assigned permissions and whether the system can detect when an agent acts outside its intended role or scope.
What makes this framework practically useful is its emphasis on monitoring and anomaly detection throughout. Rather than treating security as a pre-deployment checklist, the guide consistently emphasizes real-time detection capabilities and response mechanisms. They want you testing whether agents can flag anomalies based on behavioral tracking, whether the system generates alerts for potential security violations, and whether monitoring systems can correlate events across multiple agents to identify coordinated attacks.
From a compliance perspective, the guide acknowledges alignment with emerging regulatory frameworks including the EU AI Act and NIST AI RMF. Their testing practices emphasize accountability, explainability, and operational safety—requirements that are becoming standard across multiple jurisdictions. The forensic readiness components, including comprehensive logging and trace preservation, directly support regulatory reporting requirements and incident response obligations.
The document also introduces the concept of autonomous red teaming agents—using AI systems to detect multi-agentic security issues through intelligent pattern recognition and adaptive testing strategies. While this represents an emerging capability rather than established practice, it points toward where security testing is heading as these systems become more prevalent.
For organizations planning agentic AI deployments, this guide provides the testing framework needed to validate security assumptions before production rollout. The methodologies are detailed enough to inform both internal security teams and external red teaming engagements, while the threat categorization helps prioritize testing efforts based on specific deployment contexts and risk tolerances.
The bottom line is that agentic AI security requires a fundamentally different approach than traditional application security or even standard AI/ML security testing. The autonomous, decision-making nature of these systems creates novel attack vectors and failure modes that existing security frameworks weren't designed to address. This guide provides the roadmap for adapting our security practices to match the reality of what we're actually deploying.
TLDR: This guide, developed by the Cloud Security Alliance and OWASP AI Exchange, outlines a specialized approach for red teaming Agentic AI systems. These systems represent a significant leap beyond traditional Generative AI, capable of autonomous planning, reasoning, and acting in complex real-world and digital environments, often leveraging tools and orchestrating multi-step actions. This autonomy introduces novel security challenges including emergent, unpredictable behaviors, unstructured communication, significant interpretability challenges, and a substantially larger attack surface compared to standard LLMs.
The document underscores that traditional red teaming is insufficient due to the non-deterministic nature and independent decision-making of Agentic AI, creating unique security vulnerabilities and ethical risks that existing safeguards weren't designed to address. Therefore, early and continuous red teaming is critical to identify emerging failure modes, adversarial scenarios, and unintended consequences both pre- and post-deployment, enabling the development of more robust guardrails and safety mechanisms.
Aimed primarily at experienced cybersecurity professionals and Agentic AI developers, the guide provides actionable steps across 12 critical threat categories. These categories cover diverse areas such as: Agent Authorization and Control Hijacking, Checker-Out-of-the-Loop, Agent Critical System Interaction, Goal and Instruction Manipulation, Hallucination Exploitation, Impact Chain and Blast Radius, Knowledge Base Poisoning, Memory and Context Manipulation, Multi-Agent Exploitation, Resource and Service Exhaustion, Supply Chain and Dependency Attacks, and Agent Untraceability. The guide focuses on identifying technical weaknesses, explicitly excluding comprehensive risk assessment or detailed mitigation strategies. Proactive, evolving red teaming is deemed essential for future Agentic AI security.
