analyze-adversarial-report

Analyzing adversarial test reports involves evaluating simulation transcripts and run data to map agent failures against specific attack vectors, generating actionable remediation strategies for prompt and guardrail improvements.

An adversarial evaluation report diagnoses LLM security by mapping simulation transcript evidence to known attack vectors like prompt injection, PII leakage, and verification bypass to identify specific agent vulnerabilities.

You generate guardrail rules from red-teaming simulation transcripts by analyzing failure evidence within adversarial reports, mapping the failures to attack vectors, and producing structured policy-based refusal rules for agent hardening.

Yes, you need Coval evaluation data to map agent failures to attack vectors, because the skill requires access to Coval simulation transcripts, composite metrics, and run data to generate accurate hardening plans.

The best way to harden AI agents against system prompt extraction is to analyze adversarial sweep transcripts to generate a specific policy-based refusal rule, verifying the logic blocks the extraction attack vector.