TL;DR

Red teaming systematically attacks AI to find vulnerabilities: jailbreaks, bias triggers, harmful outputs, and edge cases. Fix issues before deployment.

Red teaming objectives

  • Find jailbreaks and safety bypasses
  • Trigger biased or harmful outputs
  • Discover edge cases and failures
  • Test robustness to adversarial inputs
  • Validate safety guardrails

Red teaming approaches

Manual: Human experts craft adversarial prompts
Automated: Generate test cases systematically
Hybrid: Humans + AI tools
Continuous: Ongoing testing in production

Attack categories

Safety bypasses: Jailbreaking, roleplaying tricks
Bias elicitation: Trigger stereotypes, unfair outputs
Information leakage: Extract training data, system prompts
Harmful content: Generate dangerous instructions
Quality failures: Nonsensical, incorrect outputs

Red teaming methodology

  1. Define threat model
  2. Create attack scenarios
  3. Execute attacks
  4. Document failures
  5. Prioritize fixes
  6. Retest after mitigations

Tools and techniques

  • Adversarial prompt libraries
  • Automated test generation
  • Fuzzing techniques
  • LLM-based attack generators

Metrics

  • Attack success rate (ASR)
  • Time to first failure
  • Coverage of attack vectors
  • Severity of discovered issues

Remediation strategies

  • Update safety training
  • Improve content filters
  • Refine system prompts
  • Add input/output validation

Best practices

  • Red team before each release
  • Diverse red team (backgrounds, perspectives)
  • Document all findings
  • Track fixes and retests
  • Continuous improvement