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Safety
Red Teaming AI
Definition
AI red teaming is the practice of systematically testing AI systems for vulnerabilities, biases, and failure modes by simulating adversarial attacks and edge cases before deployment.
Why It Matters
Developers test the happy path. Red teamers test the unhappy paths - the edge cases, adversarial inputs, and creative attacks that break systems in production. For AI systems, red teaming is essential because LLMs can fail in unexpected, creative ways that standard testing doesn’t catch.
What to Test
Safety:
- Generating harmful content
- Bypassing safety measures
- Prompt injection vulnerabilities
- Privacy leaks
Reliability:
- Edge case handling
- Graceful degradation
- Error recovery
- Context limit behavior
Bias:
- Unfair treatment of groups
- Stereotyping
- Uneven performance across demographics
How to Red Team
- Define Scope: What failures are you testing for?
- Assemble Team: Diverse perspectives find more issues
- Create Scenarios: Systematic attack patterns + creative exploration
- Document Findings: Track issues with severity and reproducibility
- Remediate: Fix issues and verify fixes
- Repeat: Continuous testing as system evolves