发现大模型会自我解禁，提出分步干预方法提升安全性
When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models
- 识别模型先觉察有害意图却在推理中自行放弃安全判断
- 新框架在关键步骤介入，使安全与推理能力兼得
- 适合关注大模型安全与可靠推理的研究者
大型推理模型在复杂多步推理任务中表现优异，但仍存在生成有害内容等严重安全问题。现有方法通常对整个推理过程施加粗粒度约束，既削弱推理能力，又未能解决根本原因。本文揭示了一种此前未被充分关注的失败模式——自我解禁（Self-Jailbreak），即模型在初始阶段能识别查询的有害意图，但在后续推理步骤中自行覆盖此判断，最终生成不安全输出。这表明模型具备识别危害的能力，而安全问题主要源于推理过程本身。为此，我们提出链式守卫（Chain-of-Guardrail, CoG）框架，通过在推理轨迹上进行目标明确的分步干预，在不损害推理能力的前提下缓解自我解禁现象。在多个安全与推理基准上的实验表明，与现有方法相比，CoG 在安全性和推理性能之间实现了更优平衡。
原文摘要 · Abstract (English)
Large Reasoning Models (LRMs) achieve strong performance on complex multi-step reasoning, yet they still exhibit severe safety failures such as harmful content generation. Existing methods often apply coarse-grained constraints over the entire reasoning trajectories, which can undermine reasoning capability while failing to address the root causes of unsafe behavior. In this work, we uncover a previously underexplored failure mode in LRMs, termed Self-Jailbreak, where models initially recognize the harmful intent of a query, but override this judgment during subsequent reasoning steps, ultimately generating unsafe outputs. Such a phenomenon reveals that LRMs are capable of recognizing harm, while safety failures primarily arise from reasoning steps. Motivated by this finding, we propose Chain-of-Guardrail(CoG), a trajectory-level training framework that mitigates Self-Jailbreak via targeted, step-level interventions while maintaining reasoning ability. Experiments across multiple safety and reasoning benchmarks indicate that CoG achieves a favorable balance between safety and reasoning performance compared with existing approaches.
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