LLM red teaming: how to turn adversarial testing into a regression suite

LLM red teaming uses controlled adversarial methods such as prompt injection, role-play, encoding, multi-turn escalation, and extraction attempts to identify policy violations, unauthorized actions, and sensitive-data exposure in AI applications. Because red team reports become outdated after model, prompt, permission, retrieval, or application changes, confirmed findings should be converted into reproducible regression tests with preserved attack context, an approved safe outcome, severity metadata, and explicit pass-or-fail criteria. Deterministic scorers can detect concrete disclosures or prohibited patterns, while calibrated LLM judges can assess nuanced semantic harms; each scorer and test case should be updated as attack variants emerge. Findings may originate from internal teams, external vendors, open-source tools, or production incidents, but require human validation before becoming release requirements. Braintrust is presented as an evaluation platform that stores adversarial datasets, runs experiments, compares results across versions, and integrates suites into CI/CD so critical regressions can block releases, while retaining evidence and coverage over time; it does not itself generate attacks or scan applications.

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