Red Teaming articles

Continuous AI red teaming in CI/CD

Turn AI security findings into repeatable CI checks with versioned cases, complete result manifests, explicit release gates, and retained evidence.

MCP security testing: tools, permissions, and untrusted content

Build MCP security tests for tool discovery, authorization, injected tool results, session identity, and approval boundaries in AI agents.

How to evaluate LLM guardrails

Compare LLM guardrails using attack blocking, legitimate task success, false refusals, latency, cost, and explicit handling of errors.

AI red teaming metrics: measuring failures and coverage

Measure AI red teaming with explicit attack success rates, attempt budgets, coverage, false refusals, severity, and evaluator uncertainty.

Red teaming RAG systems

Test RAG systems for poisoned documents, cross-tenant retrieval, stale permissions, citation leaks, and unauthorized tool actions.

How to red team AI agents

Test AI agent permissions, memory, handoffs, retries, and tool actions with a practical security matrix and reproducible evaluation workflow.

How to test prompt injection in LLM applications

Build prompt injection tests for user input, retrieved documents, and tool responses, with checks for data access and actual side effects.

What is AI red teaming?

Design scoped adversarial tests for AI applications, examine tool and retrieval boundaries, and turn findings into regression coverage.