AI red team
An AI red team is the function that attacks an organization's own AI systems under controlled conditions to produce evidence of exploitability before an outside attacker does. The team runs prompt injection payloads, jailbreak variants, data-exfiltration attempts through completions, and tool-abuse sequences against agent workflows, then documents which attempts landed, which controls fired, and what the blast radius would have been in production.
What an AI red team tests
The working test surface maps to OWASP LLM01 and its neighbors in the LLM Top 10 (2025 update): direct injection through user-controlled prompts, indirect injection through retrieval sources and tool responses, system-prompt extraction, sensitive-data elicitation from completions, and unauthorized tool calls inside agent loops. Jailbreak families get their own suites, persona injection, hypothetical framing, token-level encoding, and many-shot context flooding among them. The Stanford Trustworthy AI / AIUC-1 Consortium briefing measured refusal behaviors of model-level guardrails degrading significantly under targeted fine-tuning and adversarial pressure, which is the standing reason red teams retest after every model version change rather than certifying once.
How findings become controls
Each landed attack converts into two artifacts. A regression test case that reruns on every model or framework change, and a candidate policy rule at the request layer that blocks the payload pattern or the tool-call sequence deterministically. Per-decision audit records give the team replay material, since each record preserves what the model actually received and returned at the moment of the attempt. The retest cadence follows the deployment surface: new model versions, new tools granted to agents, and new retrieval sources each reopen the relevant suite.
Related reading
- AI Red Teaming Workflow: The Test-Fix-Prove Loop for Enterprise AI Deployments
AI red teaming discovers vulnerabilities in prompt handling, tool-call authorization, and response classification. The finding is one artifact. The fix is another. The evidence that the fix works is a third. This piece walks through a red-teaming workflow that produces all three artifacts inside the enterprise control boundary, and the inspection-layer architecture that turns findings into policy the enforcement layer executes.
- LLM Jailbreak Defense Patterns: The Layered Controls That Survive Real Production Traffic
Model-provider safety training reduces jailbreak success rates but does not eliminate them. Production deployments layer three defenses around the model: input-side classifiers that flag adversarial prompts, output-side classifiers that flag policy-violating responses, and identity-aware policy at the request boundary that limits what a successful jailbreak can accomplish. The layered pattern, the residual failure modes, and the audit record each layer produces.
- Prompt Injection Test Cases: The Twelve Patterns Your Red Team Has To Run
Prompt injection test cases for production AI deployments cluster into twelve patterns the red team has to exercise: instruction-override, role-reversal, encoded payloads, indirect injection through retrieved content, tool-output injection, multi-turn persuasion, authority impersonation, output-formatting hijack, translation pivot, long-context dilution, system-prompt extraction, and authorization-bypass. This piece walks through each pattern, the payload structure, the expected inspection-layer verdict, and the audit record the test should produce.