AI Red-Teaming: How Organizations Test AI Systems for Failure
The term comes from military and intelligence practice, where a "red team" plays the role of the adversary, probing defenses for weaknesses that the defending side, the "blue team," might not see in their own systems. The idea transferred to cybersecurity, where red teams attempt to breach systems before real attackers do. And it has transferred again to AI, where the adversary being simulated is anyone who might try to make an AI system behave badly.
That turns out to be a usefully broad category.
Standard AI evaluation involves testing a model on a benchmark dataset and measuring how often it produces correct or appropriate outputs. This is valuable but limited. Benchmarks are finite. The space of possible inputs is not. A model that performs well on every benchmark in its evaluation suite can still behave badly on inputs that weren't in the suite, and adversarial users will find those inputs. Red-teaming is an attempt to find them first.
AI red-teaming typically involves people, often a mix of internal safety researchers and external contractors, who are given a model or an AI-powered system and asked to find ways to make it fail. The failures they're looking for fall into several categories. Safety failures occur when the model produces outputs that are harmful, dangerous, or in violation of the organization's policies: instructions for creating weapons, content that sexualizes minors, detailed guidance for illegal activities. Reliability failures occur when the model produces confidently wrong outputs, hallucinates facts, or behaves inconsistently across similar inputs. Fairness failures occur when the model treats different groups of people differently in ways that are discriminatory or harmful. And security failures occur when the model can be manipulated through adversarial inputs, prompt injection attacks, or jailbreaks that bypass its safety guidelines.
Jailbreaking, the practice of finding prompts or interaction patterns that cause a model to ignore its safety training and produce outputs it would normally refuse, is one of the most extensively documented forms of adversarial AI interaction. Red teams spend significant effort trying to jailbreak models before deployment, because jailbreaks that red teams find can be addressed before release, while jailbreaks that malicious users find after release become public vulnerabilities. The cat-and-mouse dynamic between safety measures and jailbreak techniques is ongoing, which is why red-teaming isn't a one-time exercise but a continuous practice.
The people doing the red-teaming matter as much as the process. A red team composed entirely of people with similar backgrounds and assumptions will find a different, and likely narrower, set of failure modes than a diverse team. Failure modes related to specific cultural contexts, languages, or demographic groups are more likely to be found by people with firsthand knowledge of those contexts. This is one reason major AI labs have moved toward external red-teaming programs that recruit people with diverse backgrounds and domain expertise, compensating them for finding and reporting vulnerabilities the way bug bounty programs work in cybersecurity.
Automated red-teaming is an active area of research and development. Human red-teamers are expensive, slow, and limited in the volume of inputs they can test. Automated approaches use AI models to generate adversarial inputs at scale, systematically exploring the space of possible attacks more thoroughly than human testers can. These approaches have real limitations, automated red-teaming tends to find the failure modes that the automated system is designed to look for, and may miss creative or novel attacks that human testers would find, but they complement human red-teaming rather than replacing it.
Red-teaming has become an institutional expectation for frontier AI models. Anthropic, OpenAI, Google DeepMind, and Meta all conduct red-teaming before releasing major models, and the results of those exercises inform deployment decisions. The US government's AI Safety Institute has established protocols for pre-deployment testing of frontier models that include red-teaming components. The EU AI Act includes requirements for risk assessment and adversarial testing for high-risk AI systems. What was once an optional best practice is becoming a regulatory expectation.
For organizations deploying AI systems rather than building foundation models, red-teaming is equally relevant but often underinvested. A company that builds a customer-facing AI application on top of a foundation model is responsible for the behavior of that application, even if the underlying model is someone else's. The application layer introduces its own failure modes: the system prompt can be exploited, the integration with external tools can create new attack surfaces, the specific use case may involve sensitive domains that the foundation model's general red-teaming didn't focus on. Deployers who treat the foundation model's safety evaluation as sufficient for their application are making a mistake that adversarial users will eventually find.
Red-teaming doesn't make AI systems safe. It makes them safer than they would be without it, by finding and addressing failure modes before they're exploited rather than after. The practice is valuable precisely because it assumes the adversarial perspective: not asking what the system is supposed to do, but asking what a motivated, creative, and potentially malicious user could make it do. Those are different questions, and asking both is part of building AI systems that can be trusted in the real world.