Red Teaming
Red teaming in AI is the practice of systematically probing an AI system for vulnerabilities, failure modes, and harmful behaviors through adversarial testing. Red team members attempt to elicit unsafe outputs, bypass guardrails, expose biases, and discover edge cases that could cause real-world harm. The findings are used to strengthen the model's safety before public deployment.
Example
Before launching a new chatbot, a red team spends weeks trying to make it generate misinformation, reveal private training data, produce biased outputs about protected groups, and comply with requests for harmful content. Each successful exploit is documented and used to improve the model's safety training and system prompt defenses.
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