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arXiv · 2504.17704

Safety in Large Reasoning Models: A Survey

Cheng Wang·Yue Liu·Baolong Bi·Duzhen Zhang·Zhong-Zhi Li·Yingwei Ma·Yufei He·Shengju Yu·Xinfeng Li·Junfeng Fang·Jiaheng Zhang·Bryan Hooi
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Abstract

Large Reasoning Models (LRMs) have exhibited extraordinary prowess in tasks like mathematics and coding, leveraging their advanced reasoning capabilities. Nevertheless, as these capabilities progress, significant concerns regarding their vulnerabilities and safety have arisen, which can pose challenges to their deployment and application in real-world settings. This paper presents a comprehensive survey of LRMs, meticulously exploring and summarizing the newly emerged safety risks, attacks, and defense strategies. By organizing these elements into a detailed taxonomy, this work aims to offer a clear and structured understanding of the current safety landscape of LRMs, facilitating future research and development to enhance the security and reliability of these powerful models.

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Cheng Wang, Yue Liu, Baolong Bi, Duzhen Zhang, Zhong-Zhi Li, Yingwei Ma, Yufei He, Shengju Yu, Xinfeng Li, Junfeng Fang, Jiaheng Zhang, Bryan Hooi. 2025-04-24. Safety in Large Reasoning Models: A Survey. https://arxiv.org/abs/2504.17704

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