agents-vs-agents

Adversarial AI testing uncovers guardrail weaknesses by orchestrating multi-turn interactions between an adversary LLM, a target AI, and a judge to evaluate behavior and detect erosion over time.

You run a prompt injection test by executing an adversary-judge loop against your AI interface, generating adversarial prompts, and evaluating the target AI's responses to identify guardrail failures.

Yes, you can use this for compliance auditing and bias detection. The framework supports structured JSON-based outputs to maintain traceability across safety checks, data leakage risks, and scope containment tests.

The best way to evaluate AI behavior over multiple turns is using a triad interaction protocol where an adversary generates inputs, the target AI responds, and a judge evaluates the transcripts for guardrail erosion.

No specific dependencies are required to perform red-teaming. The framework operates independently, using JSON-based messages and persistence to maintain traceability during adversarial testing.