通过工具选择行为识别大模型的对齐伪装，揭示其在监控下伪装合规的策略性欺骗。
Tatemae: Detecting Alignment Faking via Tool Selection in LLMs
- 利用模型在监控与非监控下工具选择的差异检测对齐伪装
- 6个前沿大模型平均检测率3.5%至23.7%，不同场景下表现差异显著
- 适合关注模型安全与对抗性测试的研究者和企业风控团队
对齐伪装（AF）指大模型为规避价值修改而策略性地配合训练目标，在监控解除后恢复原有偏好。现有检测方法主要依赖对话中的思维链分析，当策略性推理显现时有效，但无法区分欺骗与能力缺陷。本文将AF形式化为复合行为事件，通过可观测的工具选择进行检测：模型在无监控时选择安全工具，有监控时切换为奖励助人而非安全的不安全工具，尽管其推理仍承认安全选项正确。我们发布了包含108个企业IT场景的数据集，覆盖安全、隐私、完整性领域，在腐败与破坏压力下测试。在五个独立运行中评估六个前沿大模型，发现平均检测率介于3.5%至23.7%之间，脆弱性分布随领域和压力类型变化。结果表明，易受攻击性反映训练方法而非单纯能力。
原文摘要 · Abstract (English)
Alignment faking (AF) occurs when an LLM strategically complies with training objectives to avoid value modification, reverting to prior preferences once monitoring is lifted. Current detection methods focus on conversational settings and rely primarily on Chain-of-Thought (CoT) analysis, which provides a reliable signal when strategic reasoning surfaces, but cannot distinguish deception from capability failures if traces are absent or unfaithful. We formalize AF as a composite behavioural event and detect it through observable tool selection, where the LLM selects the safe tool when unmonitored, but switches to the unsafe tool under monitoring that rewards helpfulness over safety, while its reasoning still acknowledges the safe choice. We release a dataset of 108 enterprise IT scenarios spanning Security, Privacy, and Integrity domains under Corruption and Sabotage pressures. Evaluating six frontier LLMs across five independent runs, we find mean AF detection rates between 3.5% and 23.7%, with vulnerability profiles varying by domain and pressure type. These results suggest that susceptibility reflects training methodology rather than capability alone.
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