AI Red Teaming
intermediateAdversarial testing of AI systems to find vulnerabilities, safety failures, and unintended behaviors before deployment. Red teams attempt to make models produce harmful outputs, bypass safety measures, or exhibit deceptive behavior.
Overview
AI red teaming borrows from cybersecurity the practice of attacking your own systems to find weaknesses. For AI, this means systematically trying to elicit harmful, deceptive, or unintended behaviors. Red teams test for prompt injection, jailbreaking, harmful content generation, privacy violations, bias, and emergent deceptive capabilities. The goal is to find problems before bad actors or real-world deployment does. Effective red teaming requires creativity, domain expertise, and systematic methodology. It's become a critical part of responsible AI development, often required by regulations and industry standards.
Key Concepts
Adversarial Prompting
Crafting inputs specifically designed to trigger failures or bypass safety measures.
Capability Elicitation
Testing whether models have hidden or dangerous capabilities.
Failure Mode Analysis
Systematically categorizing how and why models fail.
Continuous Red Teaming
Ongoing adversarial testing throughout model lifecycle, not just pre-deployment.
Real-World Use Cases
- 1Pre-deployment safety validation
- 2Regulatory compliance demonstration
- 3Identifying training data contamination
- 4Testing guardrail effectiveness