AI red teaming
Design authorized adversarial tests for AI applications, examine tool and retrieval boundaries, and turn findings into regression coverage.
4 guides · Suggested reading order
Start here
What is AI red teaming?
Define test boundaries, adversarial cases, and evidence for remediation.
Continue the learning path
How to test prompt injection in LLM applications
Build direct and indirect injection cases and inspect actual tool actions.
How to red team AI agents
Test permissions, memory, handoffs, retries, and execution budgets.
Continuous AI red teaming in CI/CD
Preserve reviewed findings as complete, repeatable release checks.
Go deeper
Red teaming RAG systems
Test retrieval access, poisoned documents, revocation, and citation boundaries.
AI red teaming metrics: measuring failures and coverage
Define success rates, attempt budgets, severity, coverage, and uncertainty.
MCP security testing: tools, permissions, and untrusted content
Check tool metadata, permissions, session identity, and untrusted results.
How to evaluate LLM guardrails
Measure protection alongside false refusals and legitimate task completion.
Put the foundations into practice
Explore practical guides and resources to take the next step.