Category
AI Red Teaming for LLMs and Agents
AI red teaming is the process of deliberately testing an AI system under adversarial, unusual, or high-risk conditions. For LLMs and agents, red teaming helps uncover failure modes that may not appear in standard test cases.
Red teaming is especially important when an AI system has access to tools, sensitive data, customer workflows, internal policies, or actions that can affect real business processes. A red team may probe for prompt injection, data leakage, unsafe advice, policy bypass, over-delegation, unauthorized tool use, and other harmful or unreliable behaviors.
Red teaming is different from generic evaluation. Evals measure expected behavior across a defined dataset. Red teaming actively searches for ways the system breaks. The most valuable outcome is not a list of one-off examples. The valuable outcome is a reusable failure-mode taxonomy: what failed, why it failed, how severe it was, and how similar failures should be detected in the future.
FailureModes.ai helps convert red-team findings into an ongoing reliability system. Instead of discovering risks once and losing them in a report, teams can turn them into evals, monitors, alerts, and controls that continue to operate after deployment.
In scope
Outputs of a useful AI red-team engagement
Failure-mode labels
Each finding is tagged to a recurring pattern, not a one-off.
Reproducible test cases
Every finding can be re-run on demand.
Severity ratings
Outcomes are scored by business and security impact.
Detection signals
Each finding maps to a runtime observable.
Recommended mitigations
Concrete controls, not abstract advice.
Production monitors
Findings become live detectors after the engagement.
Regression tests
Findings stay in the eval suite to prevent recurrence.
Where FailureModes.ai fits
FailureModes.ai operationalizes red-team findings: each confirmed failure becomes a labeled, severity-scored entry in your taxonomy, a regression test in your evals, and a monitor in production.