Safety in Large Reasoning Models: A Survey

• URL: http://arxiv.org/abs/2504.17704v1
• Date: Thu, 24 Apr 2025 16:11:01 GMT
• Title: Safety in Large Reasoning Models: A Survey
• Authors: Cheng Wang, Yue Liu, Baolong Li, Duzhen Zhang, Zhongzhi Li, Junfeng Fang,
• Abstract summary: Large Reasoning Models (LRMs) have exhibited extraordinary prowess in tasks like mathematics and coding, leveraging their advanced reasoning capabilities.<n>This paper presents a comprehensive survey of LRMs, meticulously exploring and summarizing the newly emerged safety risks, attacks, and defense strategies.
• Score: 15.148492389864133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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