When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913058654257152 |
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| author | Mao, Yingzhi Zhang, Chunkang Wang, Junxiang Guan, Xinyan Cao, Boxi Lu, Yaojie Lin, Hongyu Han, Xianpei Sun, Le |
| author_facet | Mao, Yingzhi Zhang, Chunkang Wang, Junxiang Guan, Xinyan Cao, Boxi Lu, Yaojie Lin, Hongyu Han, Xianpei Sun, Le |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21285 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models Mao, Yingzhi Zhang, Chunkang Wang, Junxiang Guan, Xinyan Cao, Boxi Lu, Yaojie Lin, Hongyu Han, Xianpei Sun, Le Artificial Intelligence Computation and Language 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. |
| title | When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2510.21285 |