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Agentic AI Red Teaming Guide (CSA)

Overview

Red teaming is the practice of simulating real-world adversarial attacks against a system to uncover exploitable weaknesses before malicious actors do. The concept originated in military wargaming, where a designated "red team" plays the adversary against a "blue team" defending a position, and has since been adopted across cybersecurity and, more recently, AI systems — where it now also covers probing models and agents for harmful, biased, or unsafe outputs in addition to traditional exploit-based testing (background per IBM: What is red teaming?).

The Cloud Security Alliance (CSA) Agentic AI Red Teaming Guide (published by the AI Organizational Responsibilities Working Group, August 2025) extends this practice specifically to Agentic AI systems. Traditional LLM red teaming focuses on single-turn prompt/response safety. Agentic systems plan, reason, act autonomously across multiple steps, invoke tools, retain memory, and coordinate with other agents — which means a red teaming methodology must test the full action chain, not just model outputs. The guide explicitly excludes threat modeling (covered separately by CSA's MAESTRO framework) and focuses purely on testing procedures.

Why Red Teaming Agentic AI Is Important

Agentic AI introduces unique challenges beyond single-turn LLM interactions:

  • Autonomous action — agents execute multi-step plans and invoke tools without per-step human confirmation, so a single manipulated decision can cascade into real-world consequences.
  • Reuse of existing knowledge and resources — agents draw on external knowledge bases, memory stores, and other agents' outputs, widening the attack surface beyond the model itself.
  • Emergent, compounding risk — errors, hallucinations, or injected instructions can propagate through a chain of tool calls or agent handoffs before a human ever reviews the outcome.

The 12 Threat Categories

The guide organizes its detailed testing guidance into 12 threat categories. Each category defines Test Requirements, breaks down into numbered sub-tests, and provides concrete Actionable Steps red teamers can execute.

# Category What It Tests
4.1 Agent Authorization and Control Hijacking Direct control hijacking, permission escalation, role inheritance exploitation, activity monitoring/detection gaps, separation of control vs. execution, audit trail completeness, least-privilege enforcement
4.2 Checker-Out-of-the-Loop Threshold-breach simulation, checker/human engagement testing, anomaly detection and response, context-aware decision analysis, continuous monitoring and feedback loops
4.3 Agent Critical System Interaction Physical system manipulation, IoT/OT command injection, critical infrastructure access, safety interlock bypass, real-time anomaly monitoring, command/action validation
4.4 Agent Goal and Instruction Manipulation Goal interpretation attacks, instruction-set poisoning, semantic manipulation, recursive/hierarchical goal subversion, adaptive manipulation, data exfiltration via goal drift, goal extraction attempts
4.5 Agent Hallucination Exploitation Induced hallucination testing, cascading hallucination across agents, decision manipulation via fabricated outputs, output verification/validation, anomaly detection, protective failsafes, domain-specific hallucination triggers
4.6 Agent Impact Chain and Blast Radius Cross-system exploitation via compromised agents, trust-relationship abuse, blast-radius limitation, containment mechanisms, security barrier validation
4.7 Agent Knowledge Base Poisoning Training data poisoning, external knowledge manipulation, knowledge base corruption, guided-learning exploitation, update mechanism vulnerabilities, cross-agent knowledge sharing, recovery/monitoring
4.8 Agent Memory and Context Manipulation Context amnesia exploitation, cross-session/cross-application data leakage, memory poisoning, memory overflow/context loss, secure session management
4.9 Agent Orchestration and Multi-Agent Exploitation Inter-agent communication exploitation, trust-relationship abuse, coordination protocol manipulation, feedback-loop resource exhaustion, orchestration/boundary control, adversarial collusion/spoofing between agents, orchestrator state poisoning via managed-agent responses
4.10 Agent Resource and Service Exhaustion Computational resource depletion, memory exhaustion, API/service quota depletion, learning-process exploitation, anomaly monitoring, defensive architecture testing
4.11 Agent Supply Chain and Dependency Attacks Development-chain compromise, dependency injection, service-chain compromise, deployment-pipeline security, monitoring/detection capability
4.12 Agent Untraceability Trace evasion simulation, downstream tool activation analysis, forensic analysis obfuscation, accountability chain verification, anonymization stress tests with PII/PCI/PHI

Representative Actionable Steps

A sample of the guide's testing procedures, illustrating the level of specificity:

  • Use API testing tools (Postman, Burp Suite) to inject malicious commands into an agent's control plane and observe whether unauthorized actions are accepted (4.1).
  • Simulate breaches of predefined operational thresholds (e.g., aviation operational limits) and measure how quickly the agent detects and reports checker/human-in-the-loop unavailability (4.2).
  • Use simulation tools to mimic unsafe physical conditions (e.g., overriding temperature controls) and evaluate whether the agent logs and reports anomalies before acting (4.3).
  • Provide intentionally ambiguous or conflicting goal instructions and document how the agent resolves the ambiguity (4.4).
  • Seed follow-up tasks with deliberately fabricated outputs from an earlier step and trace whether the error cascades through downstream agents (4.5, 4.9).
  • Attempt to access interconnected systems using the credentials of a deliberately compromised agent to measure blast radius (4.6).
  • Stress-test agents with use cases involving PII/PCI/PHI to confirm logs remain comprehensible without leaking sensitive data (4.12).

Testing Methodology

The guide proposes a four-phase structured approach to red teaming Agentic AI systems:

Phase Activities
Preparation Define specific test scenarios; set up protected/isolated testing environments; prepare tools and scripts
Execution Conduct step-by-step tests; document real-time observations; capture logs and metrics
Analysis Evaluate test results; identify vulnerabilities and their potential impact; prioritize findings by severity
Reporting Create detailed reports per test; develop actionable mitigation strategies; document a summary of findings for stakeholders

Suggested effectiveness metrics (human-in-the-loop and automated): - Response time to detect and block unauthorized access attempts - Percentage of successful exploits vs. total attempts - Time to containment for identified vulnerabilities - Number of undetected malicious actions during testing

MAESTRO and Tooling Landscape

The guide names MAESTRO (CSA's separate multi-layered threat-modeling framework for Agentic AI — covering evaluation/observability, deployment/infrastructure, agent frameworks, and data operations) as the complementary modeling layer to this testing guide, plus a set of emerging red-teaming tools and benchmarks:

Tool / Framework Provider Focus
MAESTRO Cloud Security Alliance Multi-layered threat modeling (not testing) for Agentic AI: anomaly/poisoned-dataset detection, deployment/infra risks, agent framework backdoors, data operation poisoning
AgentDojo ETH Zurich (SPY Lab) Dynamic evaluation of prompt injection attacks/defenses via 97 realistic tasks and 629 security test cases; measures utility preservation vs. attack success rate
Agent-SafetyBench Academic 349 interactive environments, 2,000 test cases across 8 risk categories; automated scoring model (91.5% accuracy); evaluates 16+ LLM agents
AgentFence Open source Automated probing for prompt injection and secret leakage; prebuilt role-confusion/instruction-leakage attacks; SDK support for LangChain and OpenAI agents
SplxAI Agentic Radar SplxAI Security scanner for LLM agentic workflows: workflow visualization, tool identification (MCP, email, JIRA, etc.), vulnerability mapping report
Agent Security Bench (ASB) ICLR 2025 Evaluates 27 attack/defense methods across 10 scenarios; benchmarks 13 LLMs (84.3% max attack success rate); introduces a utility-security balance metric
Promptfoo LLM Security DB Promptfoo Structured repository of security vulnerabilities for LLMs and Agentic AI
Pentest Copilot Bugbase AI-driven red teaming automation: contextualized attack orchestration, continuous learning from threat intel, dynamic adversary campaign generation
AI Red Teaming Agent Microsoft (Azure AI Foundry) Automated content-safety risk scans; Attack Success Rate (ASR) metrics; deployment-readiness reporting
FuzzAI Framework Salesforce Automated, scalable AI security fuzzing: context-specific input generation (Mutator module), many-shot jailbreaking support, feature-specific attack strategy selection

Future Outlook

The guide identifies priority areas for the evolution of Agentic AI red teaming:

  • Autonomous red teaming agents — adaptive, automated vulnerability discovery and adversarial simulation in real time
  • Downstream action red teaming — automated, multi-domain testing of agentic action chains (beyond manual static/log-based mapping)
  • Secure multi-agent orchestration research — trust boundaries, privilege separation, and inter-agent communication security at scale
  • Standardized metrics and benchmarks — Mean Time to Detection (MTTD), exploit success rates, containment time, comparable across environments
  • Alignment with regulatory frameworks — EU AI Act, NIST AI RMF, and similar emerging requirements around accountability and explainability
  • Open-source security tooling and research — community-driven simulation frameworks and attack scenario generators

See Also

References