Red teaming: Red teaming is the practice of deliberately attacking an AI system, by trying to make it produce harmful, false, or policy-violating output, in order to find weaknesses before real users or bad actors do.
The term comes from the military and cybersecurity, where a 'red team' plays the adversary. Applied to AI, red teamers spend their time trying to make a model misbehave: coax it into giving dangerous instructions, trick it into revealing confidential data, get it to insult a customer, or slip hidden instructions into a document it will read. Every success becomes a bug report that the builders fix or fence off with guardrails.
Major labs red-team their models before release, often with outside experts in areas like biosecurity and cybersecurity, and they publish some of what they found in model cards and system cards. But a lab's red team tests the model in general. It does not test your chatbot, your prompt, your connected data, or your customers' particular ways of pushing buttons. That part is yours.
You do not need specialists to start. Before launching an AI feature, get a few colleagues, ideally the most skeptical ones, and give them an afternoon and one instruction: break it. Ask it about competitors, feed it a rude message, paste a document containing 'ignore your previous instructions,' ask for a discount it is not allowed to give, and ask for a medical opinion. Write down everything that goes wrong.
Example at work
A customer service manager is about to launch an AI assistant on the company website. Before go-live, she asks three reps to spend an hour trying to get it to promise refunds, reveal other customers' order details, and comment on a lawsuit in the news. It agrees to a refund on the first try. The team adds a guardrail and a handoff rule, then tests again.
Why it matters
Every AI feature will be probed by someone, and it is far better that the first person to find a hole is on your payroll. Red teaming is cheap, catches embarrassing and costly failures early, and is increasingly expected by customers, auditors, and regulators.
Related terms
- AI safetyAI safety is the field concerned with preventing AI systems from causing harm, from everyday failures like confident errors and biased outputs to misuse by bad actors and risks from highly capable future systems.
- GuardrailsGuardrails are the rules, filters, and technical limits placed around an AI system to keep its behavior within acceptable bounds, such as refusing harmful requests, staying on topic, protecting data, or requiring approval before acting.
- JailbreakA jailbreak is a prompt or technique designed to get an AI model to ignore its safety training and produce content or behavior it was built to refuse.
- Prompt injectionPrompt injection is an attack in which instructions hidden inside content an AI system reads, such as a web page, email, or document, hijack the system into doing something its user never asked for.
- Model evaluationModel evaluation is the process of measuring how well an AI model performs on a defined set of tasks, using test data, benchmarks, human ratings, or automated checks, to decide whether it is accurate and safe enough for a given use.