red-team-protocol

Adversarial testing for jailbreaking attempts applies a structured red team decision tree to simulate attacks and generate evidence-based reports on system robustness and data exfiltration risks.

AI threat modeling is a structured methodology to identify vulnerabilities in AI systems, needed for robustness checks and safety audits prior to production deployment to reduce operational risk.

Run a systematic adversarial evaluation by syncing project context via .claude/ai-context.md, then apply structured red-team workflows to detect output manipulation and data exfiltration risks.

Yes, you can use this methodology for data exfiltration simulations and output manipulation detection, applying context-aware evaluation aligned with safety and governance requirements.

Yes, adversarial testing requires syncing with project context via .claude/ai-context.md to initialize the red-team testing workflows and ensure context-aware evaluation.

The best way to document jailbreak testing evidence is using a structured red team decision tree that generates reproducible findings aligned with your project's safety and governance requirements.