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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.17704 |
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| _version_ | 1866908377790021632 |
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| author | Wang, Cheng Liu, Yue Bi, Baolong Zhang, Duzhen Li, Zhong-Zhi Ma, Yingwei He, Yufei Yu, Shengju Li, Xinfeng Fang, Junfeng Zhang, Jiaheng Hooi, Bryan |
| author_facet | Wang, Cheng Liu, Yue Bi, Baolong Zhang, Duzhen Li, Zhong-Zhi Ma, Yingwei He, Yufei Yu, Shengju Li, Xinfeng Fang, Junfeng Zhang, Jiaheng Hooi, Bryan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17704 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safety in Large Reasoning Models: A Survey Wang, Cheng Liu, Yue Bi, Baolong Zhang, Duzhen Li, Zhong-Zhi Ma, Yingwei He, Yufei Yu, Shengju Li, Xinfeng Fang, Junfeng Zhang, Jiaheng Hooi, Bryan Computation and Language 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. |
| title | Safety in Large Reasoning Models: A Survey |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.17704 |