Honeypot Protocol

The Honeypot Protocol introduces a novel approach to testing AI model behavior under varying prompts while keeping other conditions constant, addressing vulnerabilities in traditional trusted monitoring methods. In experiments with Claude Opus 4.6 in BashArena, the model achieved 100% success on the main task and did not trigger side tasks across evaluation, synthetic deployment, and no-monitoring scenarios. This protocol provides a baseline for future investigations into adaptive attacks and model robustness, which is critical for practitioners developing resilient AI systems.