Definition

Red teaming means putting an AI model through tough tests to see where it fails. Teams try to trick the AI, make it give bad answers, or find safety risks—just like a hacker might. It helps fix problems before the AI is released.

Example

A red team might ask an AI how to make a dangerous substance to see if it follows safety rules.

How It’s Used in AI

Used by companies like OpenAI, Anthropic, and Google DeepMind to find and fix flaws in large models. Red teaming helps prevent AI from spreading misinformation, giving unsafe answers, or leaking private info. It’s a key step in AI safety and evaluation.

Brief History

Red teaming comes from cybersecurity but was adapted to AI in the 2020s. As LLMs became more powerful, labs created internal teams and hired external experts to run adversarial tests on new models.

Key Tools or Models

Practices include manual stress testing, adversarial prompts, scenario simulations, and automated attack generation. Used in models like GPT-4, Claude, and Gemini before public release.

Pro Tip

Red teaming isn’t just about breaking AI—it’s about building better AI. The more edge cases you test, the safer and smarter your model becomes.