AI Red Teaming
Incident ResponseDefinition
A structured adversarial evaluation process for AI systems that probes for safety failures, harmful output generation, bias, jailbreaks, and security vulnerabilities — performed before and after deployment.
Technical Details
AI red teaming combines traditional penetration testing with AI-specific attack vectors: prompt injection, jailbreaking, data extraction, model inversion, and training data reconstruction. Organizations like MITRE (ATLAS framework) and NIST provide structured frameworks. Red teamers use both automated fuzzing tools and creative manual prompting to find failure modes that automated safety testing misses.
Practical Usage
AI developers and enterprise deployers should conduct red team exercises before production launches, assigning teams to adversarially probe models for policy violations, factual errors, harmful outputs, and data leakage. Findings inform RLHF safety training updates, content filtering improvements, and system prompt hardening.
Examples
- A financial services firm red teams its AI advisor for investment advice that could constitute unlicensed financial guidance.
- A healthcare AI provider hires external red teamers to probe its diagnostic model for demographic bias and incorrect diagnoses.
- A security vendor red teams its AI-powered malware detection model to find evasion techniques before adversaries do.