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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
TL;DR
LRMs’ explicit reasoning and expanding deployment create safety risks that existing LLM-focused analyses do not fully address. This survey organizes LRM-specific risks, attacks, and defenses into a taxonomy, concluding that output-focused alignment is insufficient when harmful reasoning persists internally. It therefore supports reasoning-aware evaluation and alignment approaches.
Problem
LRMs’ enhanced capabilities and deployment in high-stakes domains create safety challenges not fully covered by existing LLM safety surveys.
Method
The survey categorizes inherent risks, adversarial attacks, and defense strategies specific to reasoning-enhanced models.
Results
The survey finds that exposed reasoning chains create new attack surfaces and that harmful reasoning can persist internally despite safe final outputs.
Takeaways & Limitations
LRM safety research should consider entire reasoning trajectories and develop standardized vulnerability benchmarks and human-in-the-loop alignment.
Takeaways & Limitations
The rapidly evolving field and reliance on published literature may leave the taxonomy outdated and omit proprietary industry safety research.
Abstract
from arXiv · showhide
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.
1 Introduction
LRMs extend language-model reasoning through explicit, structured inference, achieving strong performance while creating safety challenges that require dedicated analysis. This survey organizes those challenges into inherent risks, attacks, defenses, and future directions.
- Reasoning methods evolved from Chain-of-Thought prompting to self-critique, plan-and-solve, debate, and structural deliberation.
- LRMs produce human-readable reasoning traces and achieve strong results in mathematics, question answering, and code generation.
- Because LRMs are entering high-stakes domains, their safety, robustness, and alignment require dedicated assessment beyond existing LLM safety surveys.
- The survey distinguishes inherent non-adversarial risks from deliberate attacks, then examines defenses and future research directions.
2 Background
Reinforcement learning has become central to LRM development by exploring diverse reasoning paths and optimizing performance on complex multi-step tasks. RL-optimized models can develop Long-CoT capabilities beyond pure supervised fine-tuning baselines.
- Reinforcement learning optimizes LRM decision-making through environmental interaction and reward feedback.
- Reinforced Fine-Tuning explores multiple reasoning trajectories per query instead of optimizing a single Chain-of-Thought pathway.
- RL-based reasoning is effective on complex code-generation, legal-judgment, and mathematical problem-solving tasks.
- RL-optimized LRMs exhibit emergent Long-CoT capabilities that surpass pure SFT baselines.
3 Safety Risks of LRMs
LRMs present inherent safety risks during ordinary use, including unsafe compliance, agentic misbehavior, multilingual disparities, and multimodal vulnerabilities. The survey synthesizes evidence that stronger reasoning can expose or amplify these risks.
- LRM safety risks during standard use include unsafe request compliance, agentic behaviors, multilingual disparities, and multimodal challenges.
- Reasoning outputs can pose greater safety concerns than final answers, with open-source and closed-source models showing a significant safety gap.
- Stronger reasoning abilities can increase catastrophic behavior risks in high-pressure scenarios rather than mitigate them.
- 3.3 Multi-lingual Safety Risks: 21.7% discrepancy separates average English and Chinese attack success rates for DeepSeek models, indicating incomplete cross-language safety generalization.
- 3.3 Multi-lingual Safety Risks: Spanish testing found biased or unsafe response rates reaching 31.7% for DeepSeek-R1, while Chinese evaluations identified culturally specific safety deficiencies.
- 3.4 Multi-modal Safety Risks: Acquiring reasoning capabilities in multimodal reasoning models can degrade inherited safety alignment, although some models show nascent self-correction.
4 Attacks on LRMs
LRM attacks target reasoning length, answer correctness, prompts, and jailbreak resistance, exploiting explicit multi-step deliberation as a new attack surface. Reported attacks can induce inefficiency, premature conclusions, reasoning corruption, or harmful outputs.
- The survey groups LRM attacks into reasoning length, answer correctness, prompt injection, and jailbreak attacks.
- 4.1 Reasoning Length Attacks: Attackers can force overthinking or shortcut necessary deliberation because LRMs explicitly perform multi-step reasoning.
- 4.1 Reasoning Length Attacks: 70× more tokens than necessary can be generated on straightforward tasks, while performance may degrade beyond certain reasoning-length thresholds.
- 4.1 Reasoning Length Attacks: Think Less attacks force premature conclusions by reducing inference-time computation, and abnormal compute usage can reveal them.
- 4.2 Answer Correctness Attacks: Endpoint token manipulation can cause incorrect answers or complete reasoning cessation in models such as DeepSeek-R1.
- 4.3 Prompt Injection Attacks: Increasing inference-time compute improves prompt-injection robustness, while proprietary o3-mini shows nearly 80% lower direct-injection vulnerability than open-source counterparts.
- 4.4 Jailbreak Attacks: Mousetrap achieves success rates up to 98%, while H-CoT reduces rejection rates from 98% to below 2% by manipulating reasoning.
5 Defenses for LRMs
The survey organizes LRM defenses into safety alignment, inference-time defenses, and guard models. These approaches address reasoning-specific risks, but alignment can compromise reasoning capability and motivate defenses that avoid modifying the victim model.
- The survey categorizes LRM defenses into Safety Alignment, Inference-Time Defenses, and Guard Models.
- Safety Alignment: Existing LLM and VLM alignment methods may inadequately address LRMs because they overlook the reasoning process.
- Safety Alignment: Reasoning-focused safety alignment uses curated safe CoT data, supervised fine-tuning, and reinforcement learning to improve model safety.Examples include STAR-1, SafeChain, safety-aware reasoning trajectories, deliberative alignment, STAIR, SaRO, and R2D.
- Safety Alignment: Safety alignment can compromise fundamental LRM capabilities such as reasoning, creating a safety alignment tax.This motivates defenses that do not directly modify the victim model.
- Inference-Time Defenses: Inference-time defenses adapt safe prompting, demonstrations, and decoding while adding reasoning-specific techniques such as inference-time scaling and safe decoding.Inference-time scaling has been reported to improve safety and adversarial robustness, while decoding methods verify or guide reasoning without additional training.
- Guard Models: Guard models moderate victim-model inputs and outputs without retraining the victim or changing its inference strategy.Reasoning-based guards use reasoning SFT, hard-sample DPO, or critique-augmented fine-tuning to improve moderation performance, generalization, and explainability beyond classifier-based guards.
6 Future Directions
The survey identifies standardized evaluation benchmarks as a priority for testing reasoning-specific vulnerabilities and the safety and robustness of LRMs’ multi-step reasoning processes.
- Researchers should develop standardized benchmarks focused on reasoning-specific vulnerabilities in LRMs.The survey notes that current evaluation frameworks do not comprehensively test safety and robustness across multi-step reasoning processes.
7 Conclusion
The survey argues that LRMs’ exposed reasoning chains create attack surfaces beyond final outputs, while output-focused alignment can miss harmful internal reasoning. It therefore highlights trajectory-aware safety research and evaluation.
- Exposed reasoning chains create attack surfaces in which adversaries can manipulate intermediate steps through reasoning-based backdoors and hijacking.
- Traditional output-focused alignment methods can be insufficient because harmful reasoning may persist internally even when final outputs appear safe.
- The survey calls for specialized safety research, standardized benchmarks, and human-in-the-loop methods that inspect and refine reasoning traces.
Limitations
The survey’s taxonomy may become outdated as LRM research evolves rapidly, and its reliance on published literature may omit proprietary industry safety work.
- Rapid advances in LRMs may require revisions to the survey’s taxonomy and findings as new research emerges.
- Reliance on published academic literature may leave gaps in coverage of proprietary safety measures developed within companies.