Red Teaming

Red teaming is the practice of deliberately attempting to provoke failures in a system — whether an AI model, a military plan, or a software architecture — in order to discover its weaknesses before an adversary does. In AI safety, red teams construct adversarial inputs, deceptive prompts, and edge-case scenarios that stress-test models beyond their training distribution. The practice is analogous to the method of doubt in epistemology: rather than trusting a system's surface competence, the red teamer systematically doubts it.

Red teaming is not merely testing; it is adversarial testing, in which the tester is actively trying to break the system rather than confirm its functionality. This distinction matters because standard evaluation metrics — accuracy, perplexity, reward scores — are optimized for average-case performance, while safety-critical failures occur in the tails of the distribution. A red teamer's goal is to find the tails.

The rise of large language models has made red teaming a central activity in AI governance. Adversarial training is one response to red team findings, but the deeper challenge is that red teams themselves may be outpaced by the systems they test — the scalable oversight problem in practice.