The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1
Authors: name: Kaiwen Zhou, name: Chengzhi Liu, name: Xuandong Zhao, name: Shreedhar Jangam, name: Jayanth Srinivasa, name: Gaowen Liu, name: Dawn Song, name: Xin Eric Wang

The rapid development of large reasoning models, such as OpenAI-o3 and
DeepSeek-R1, has led to significant improvements in complex reasoning over
non-reasoning large language models~(LLMs). However, their enhanced
capabilities, combined with the open-source access of models like DeepSeek-R1,
raise serious safety concerns, particularly regarding their potential for
misuse. In this work, we present a comprehensive safety assessment of these
reasoning models, leveraging established safety benchmarks to evaluate their
compliance with safety regulations. Furthermore, we investigate their
susceptibility to adversarial attacks, such as jailbreaking and prompt
injection, to assess their robustness in real-world applications. Through our
multi-faceted analysis, we uncover four key findings: (1) There is a
significant safety gap between the open-source R1 models and the o3-mini model,
on both safety benchmark and attack, suggesting more safety effort on R1 is
needed. (2) The distilled reasoning model shows poorer safety performance
compared to its safety-aligned base models. (3) The stronger the model's
reasoning ability, the greater the potential harm it may cause when answering
unsafe questions. (4) The thinking process in R1 models pose greater safety
concerns than their final answers. Our study provides insights into the
security implications of reasoning models and highlights the need for further
advancements in R1 models' safety to close the gap.