How to Run a Purple Team Exercise for AI Systems
A structured guide for security teams testing AI agent permissions, tool-call governance, and runtime policy enforcement through collaborative offensive and defensive exercises.
What Distinguishes AI Purple Teaming from Traditional Exercises
Traditional purple team exercises pair a red team executing adversarial techniques with a blue team validating detection and response, typically against deterministic network, endpoint, or application behavior. AI agent systems break that determinism. A large language model can produce different outputs from the same input across runs, and its behavior can be steered through natural language embedded in user input, retrieved documents, or tool responses rather than through code-level exploits alone.
This changes what a purple team exercise needs to test. Instead of confirming whether a known exploit triggers a known signature, the exercise must confirm whether an agent’s permissions, tool-call governance, and runtime policy enforcement hold up when the model itself is manipulated. The OWASP Top 10 for LLM Applications treats prompt injection and excessive agency as leading risk categories precisely because they exploit the gap between model behavior and system-level controls. A meaningful AI purple team exercise tests both the model’s susceptibility to manipulation and the runtime controls that are supposed to contain the consequences when manipulation succeeds.
Core Testing Dimensions
Effective AI purple team exercises center on four dimensions that blue and red teams can jointly measure.
Agent Identity
Validate identity assertions and permission scoping under adversarial conditions.
Tool-Call Governance
Test whether tool approval and logging catch unauthorized or excessive actions.
Runtime Policy Enforcement
Confirm policy interception points hold when an agent is manipulated.
Detection Validation
Measure whether the blue team can actually see and respond to red team actions.
Prerequisites Before the Exercise
Without the following instrumentation, findings are difficult to attribute and hard to remediate. Confirm these capabilities exist before scheduling the exercise.
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Agent identity and permission scoping
A distinct, verifiable identity and explicit permission scope per agent, enabling spoofing and impersonation tests.
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Tool-call logging
Detailed tool-call logs showing what was requested, granted, and executed, so detection can be validated.
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Runtime policy enforcement points
Identifiable tool-call gateways or permission brokers that intercept agent actions against policy.
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Reasoning and execution separation
A separation between the agent’s reasoning layer and tool-execution layer so interception can be tested independently.
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Untrusted content isolation
Architectural isolation of retrieved documents and external responses from privileged execution paths.
Attack Techniques to Prioritize
Not every AI-specific attack technique deserves equal exercise time. Prompt injection should be tested in both its direct form, where malicious instructions arrive through user input, and its indirect form, where they arrive through retrieved documents, tool outputs, or other content the agent ingests without user visibility. OWASP separates these vectors because they require different mitigations and different test scenarios.
Excessive agency is the second priority category. OWASP defines it as the combination of excessive functionality, excessive permissions, and excessive autonomy granted to an LLM-based system, and each dimension can be tested independently: whether the agent can invoke tools it does not need, whether it holds permissions beyond what those tools require, and whether it can take multiple consequential actions without a checkpoint.
OWASP’s Agentic AI guidance extends this with agent-specific risks including tool misuse, memory poisoning, and agent identity impersonation, all of which involve an agent acting outside its intended trust boundary rather than a single bad output. MITRE ATLAS provides a shared tactic and technique taxonomy for structuring these scenarios against documented case studies, while NIST’s adversarial machine learning taxonomy offers consistent terminology for classifying findings across evasion, poisoning, privacy, and abuse categories. Mapping scenarios to both taxonomies before execution makes findings comparable across exercises and easier to track over time.
Scenario design checklist
- Map findings to MITRE ATLAS techniques or OWASP LLM Top 10 categories.
- Confirm tool-call activity, agent identity, and permissions are logged and exposed.
- Cover both direct and indirect prompt injection vectors in scenario design.
- Test excessive functionality, permissions, and autonomy as separate failure modes.
- Align findings and remediation actions with NIST AI RMF functions.
Governance Considerations
An exercise produces limited value if its findings sit in a report rather than driving accountable remediation. NIST’s AI RMF Govern function calls for defined organizational accountability structures around AI risk testing, which means an AI purple team exercise needs a named control owner for each finding, not just a security team sign-off.
Least-privilege enforcement for agent tool permissions is one of the more testable governance controls available, since OWASP treats excessive permissions as a distinct, measurable dimension of excessive agency rather than a general principle. CISA and NSA guidance on deploying AI systems securely recommends continuous testing across the AI system lifecycle rather than a single assessment, which has direct implications for how often an exercise should repeat: agent permissions, tool integrations, and underlying models change frequently enough that a one-time exercise has a short shelf life.
Recording exercise outcomes as auditable remediation tied to accountable owners, consistent with the RMF Manage function, gives the next exercise a baseline to measure against instead of starting from zero.
Test Your AI Agent Runtime Controls
A purple team exercise is only as effective as the runtime telemetry available to validate it. Explore how runtime governance provides the agent identity, permission scoping, and tool-call logging needed to run one.
Explore Runtime Governance