AI red teaming: what it is and how to run it for LLM products

AI red teaming is structured adversarial testing: people deliberately try to make an AI system fail in ways that matter — harmful output, policy violations, data leakage, unsafe actions — before attackers or ordinary users find those failures in production.

Scope first

Define the system under test (model, app, agent), the threat model (curious users, malicious users, competitors, automated attacks) and the harms you care about most. A customer-support bot and a code agent need very different red-team plans.

Common attack categories

Jailbreaks and policy bypasses; direct and indirect prompt injection; system prompt and data extraction; harmful, biased or defamatory content; dangerous instructions; hallucinated facts presented confidently; tool misuse and unauthorised actions in agents; and multilingual or encoded attacks that slip past English-only filters.

Manual and automated together

Automated attack generation provides breadth and regression coverage. Skilled human red teamers provide creativity, domain knowledge and the multi-turn social engineering that automated tools still miss. Use both.

Diverse testers find more

Include people with different languages, cultures, domains and adversarial mindsets. Many real-world failures appear only in a specific language, dialect or professional context.

Reporting that drives fixes

Every finding should include exact reproduction steps, the observed output, severity, the policy or risk it violates and a suggested mitigation. Group findings by root cause so teams fix classes of failure, not single prompts, and add each finding to an automated regression set.

ZenXData runs scoped AI red-teaming sprints for LLM applications and agents, with reproducible findings and retest support.