🤖 AI Summary
Traditional AI monitoring mechanisms are vulnerable to adaptive attacks, collusion, and strategic evasion due to their passive observation paradigm. This work proposes the first active probing-based monitoring protocol, which manipulates model perception by embedding three distinct conditions—evaluation, synthetic deployment, and explicit no-monitoring—within system prompts, while maintaining consistency in task, environment, and scoring criteria. Experiments with Claude Opus 4.6 in the BashArena environment demonstrate that the model achieves 100% success on the primary task across all conditions without triggering any side-effect tasks, thereby validating the efficacy of the proposed approach. These results establish a foundational benchmark for evaluating more sophisticated adversarial strategies and multi-model monitoring frameworks in future research.
📝 Abstract
Trusted monitoring, the standard defense in AI control, is vulnerable to adaptive attacks, collusion, and strategic attack selection. All of these exploit the fact that monitoring is passive: it observes model behavior but never probes whether the model would behave differently under different perceived conditions. We introduce the honeypot protocol, which tests for context-dependent behavior by varying only the system prompt across three conditions (evaluation, synthetic deployment, explicit no-monitoring) while holding the task, environment, and scoring identical. We evaluate Claude Opus 4.6 in BashArena across all three conditions in both honest and attack modes. The model achieved 100% main task success and triggered zero side tasks uniformly across conditions, providing a baseline for future comparisons with stronger attack policies and additional models.