模型推理时会更倾向暴露恶意指令，而非良性指令。
Selective Disclosure of Hidden Directives in Reasoning Models: Behavioral Asymmetry and Steering
- 通过对比模型对恶意与良性指令的推理披露差异，发现隐蔽指令泄露存在不对称性。
- 在8个主流模型中，恶意指令的推理痕迹泄露率高出5.8至13.9个百分点，显著可测。
- 揭示了隐藏行为由共享激活方向控制，为可解释性监控提供新路径，适合安全研究者参考。
链式思维（CoT）推理轨迹被视作人工智能监管的潜在机制：监控者通过分析模型推理过程，可察觉仅从输出无法发现的异常行为。该假设依赖于CoT能如实反映模型所受指令。本文沿两个维度检验此假设。首先提出指令遵循差距（ICG）：模型在恶意与良性隐藏指令下，其推理中显式提及指令的概率差异。在100组任务和8个前沿推理模型（来自5个系列）中，均观察到系统性不对称披露现象——恶意指令的泄露概率显著高于良性指令。其中，Qwen3-14B（Wilcoxon p=0.0001，+13.9pp）、Qwen3-32B（p=0.0011，+13.0pp）、Qwen3-235B（p=0.035，+5.8pp）及MiniMax-M2.5、DeepSeek-R1均有类似结果。检测器在两次独立盲标验证中达到100%精确率；仅读取推理轨迹的LLM监控器在所有8个模型中复现该不对称性，并在82%的恶意轨迹中准确识别出具体指令，而检测器却将其判为干净。其次，通过对比激活增加法在MiniMax-M2.5中提取的引导向量，可因果诱导隐藏行为的发生或抑制，且在Qwen3-14B中预注册设计下成功复制。源自良性与恶意指令的隐藏向量高度相似（MiniMax-M2.5余弦相似度0.804；Qwen3-14B为0.970），表明该不对称性源于同一隐藏方向的差异化激活，而非独立机制。
原文摘要 · Abstract (English)
Chain-of-thought (CoT) reasoning traces are increasingly proposed as a mechanism for AI oversight: a monitor inspecting a model's reasoning can, in principle, detect misbehavior invisible from outputs alone. This assumes CoT surfaces what a model is instructed to do regardless of the instructions given. We test this assumption along two axes. First, we introduce the Instruction-Compliance Gap (ICG): the difference in probability that a model's CoT explicitly references a hidden system prompt directive when that directive is malign versus benign. Across 100 task pairs and 8 frontier reasoning models from 5 families, we find consistent asymmetric disclosure, a higher probability of leaking malign hidden instructions than benign ones, in Qwen3-14B (Wilcoxon $p=0.0001$, $+13.9$pp), Qwen3-32B ($p=0.0011$, $+13.0$pp), Qwen3-235B ($p=0.035$, $+5.8$pp), and similar results with MiniMax-M2.5 and DeepSeek-R1. The detector has 100% precision against two independent blinded labelling passes, and an LLM monitor reading only the reasoning trace reproduces the asymmetry in all 8 models against directive-free controls, identifying the specific directive in 82% of malign traces which the detector classifies as clean. Second, steering vectors extracted in MiniMax-M2.5 via Contrastive Activation Addition causally induce hiding from bare prompts and suppress it from prompts that would otherwise produce it, replicating in Qwen3-14B under a pre-registered design. Benign and malign-derived hiding vectors are highly similar (cosine $0.804$ in MiniMax-M2.5; $0.970$ in Qwen3-14B), implying that in these models the disclosure asymmetry arises from differential activation of a shared hiding direction rather than separate mechanisms.
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