To fix AI, first break it: Red teaming for AI safety

Artificial intelligence is transforming society at an unprecedented pace, from generative chatbots in customer service to algorithms aiding medical diagnoses.

- POOJA ARORA

Artificial intelligence is transforming society at an unprecedented pace, from generative chatbots in customer service to algorithms aiding medical diagnoses. Along with this promise, however, come serious risks—AI systems have produced biased or harmful outputs, revealed private data, or been 'tricked' into unsafe behavior. In one healthcare study, for example, red-team testing found that roughly one in five answers from advanced AI models like GPT-4 was inappropriate or unsafe for medical use.

To ensure AI's benefits can be realized safely and ethically, the tech community is increasingly turning to red teaming—a practice of stress-testing AI systems to identify flaws before real adversaries or real-world conditions do.

In simple terms, red teaming is about playing 'devil's advocate' with AI systems—actively trying to break, mislead, or misuse them to expose weaknesses.

Originally a military and cybersecurity concept, red teaming refers to an adversarial testing effort where a 'red team' simulates attacks or exploits against a target, while a 'blue team' defends. In the AI context, AI red teaming means probing AI models and their surrounding systems for vulnerabilities, harmful behaviors, or biases by emulating the strategies a malicious or curious attacker might use.

In essence, a red teamer tries to ask, 'How could this AI go wrong or be made to do something bad?' and then systematically tests those scenarios. Red teaming in AI goes beyond just the model's answers—it can involve examining the whole pipeline (data, infrastructure, user interface) for weaknesses. As modern AI models are open-ended and creative by design, they can also be creatively misused.