Safety in Large Reasoning Models: A Survey
Cheng Wang, Yue Liu, Baolong Bi, Duzhen Zhang, Zhong-Zhi Li, Yingwei Ma, Yufei He, Shengju Yu
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 the first comprehensive survey of LRMs, meticulously exploring and summarizing the newly emerged safety risks, attacks, and defense strategies specific to these powerful reasoning-enhanced models. 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.
BibTeX
@inproceedings{emnlp2025_safetyinlargerea,
title = {Safety in Large Reasoning Models: A Survey},
author = {Cheng Wang and Yue Liu and Baolong Bi and Duzhen Zhang and Zhong-Zhi Li and Yingwei Ma and Yufei He and Shengju Yu and Xinfeng Li and Junfeng Fang and Jiaheng Zhang and Bryan Hooi},
booktitle = {EMNLP 2025},
year = {2025}
}