Honeypot Protocol
TLDR
The honeypot protocol introduces an active testing method to detect context-dependent AI behavior, addressing passive monitoring vulnerabilities.
Key contributions
- Introduces the honeypot protocol to actively test for context-dependent AI behavior.
- Addresses vulnerabilities of passive AI monitoring to adaptive attacks and collusion.
- Tests AI by varying only system prompts across three conditions while holding tasks constant.
- Evaluates Claude Opus 4.6 in BashArena, establishing a baseline for future comparisons.
Why it matters
This paper introduces a novel active testing protocol to overcome the limitations of passive AI monitoring, which is crucial for robust AI control. By proactively probing AI behavior under varied perceived conditions, it offers a more secure approach against sophisticated adaptive attacks. This work sets a vital baseline for future research in AI safety and security.
Original 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.
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