| Challenge: | Large Reasoning Models have demonstrated outstanding capabilities in solving complex reasoning tasks by incorporating step-by-step chain-of-thought (CoT) reasoning. |
| Approach: | They evaluate three large reasoning models that perform explicit and coherent reasoning under conflicting objectives and use them to evaluate their performance. |
| Outcome: | The proposed models perform explicit and coherent reasoning before producing their outputs, improving problem-solving and multi-step decision making. |
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Safety in Large Reasoning Models: A Survey (2025.findings-emnlp)
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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
| Challenge: | Large Reasoning Models (LRMs) have a high level of advanced reasoning capabilities, but they are vulnerable and vulnerable. |
| Approach: | This paper presents the first comprehensive survey of Large Reasoning Models . it explores the new safety risks, attacks, and defense strategies specific to LRMs based on reasoning . |
| Outcome: | The proposed study examines the safety and security risks of large reasoning models. |
HAUNTATTACK: When Attack Follows Reasoning as a Shadow (2026.findings-acl)
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| Challenge: | Emerging Large Reasoning Models (LRMs) excel in mathematical and reasoning tasks, showcasing remarkable capabilities. |
| Approach: | They propose a framework that embeds harmful instructions into reasoning questions . they evaluate 11 LRMs and observe an average attack success rate of over 70% . |
| Outcome: | The proposed framework improves reasoning models by 13 percentage points over baseline. |
When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models (2026.findings-acl)
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Yingzhi Mao, Chunkang Zhang, Junxiang Wang, Xinyan Guan, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
| Challenge: | Existing methods often apply coarse-grained constraints over entire reasoning trajectories . Existing approaches often apply unsafe constraints, causing unsafe outputs . |
| Approach: | They propose a trajectory-level training framework that mitigates Self-Jailbreak . they propose 'chain-of-guardrail' to mitigate self-jailbreak by targeting step-level interventions . |
| Outcome: | The proposed framework mitigates Self-Jailbreak by targeting step-level interventions while maintaining reasoning ability. |
Self-Reflection Improves Safety of Large Reasoning Models (2026.findings-acl)
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| Challenge: | Existing safety alignment methods are shallow and do not address deeper risks and attacks in reasoning processes. |
| Approach: | They propose a technique that introduces a special Self-Reflection token to enable LRMs to perform self-reflection during generation and recover from harmful outputs. |
| Outcome: | The proposed approach outperforms the baseline model in terms of safety and helpfulness, and significantly improves model safety without adversarial training. |
Reasoning Hijacking: The Fragility of Reasoning Alignment in Large Language Models (2026.acl-long)
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| Challenge: | Current LLM safety research focuses on mitigating **Goal Hijacking**, preventing attackers from redirecting a model’s high-level objective. |
| Approach: | They propose a new adversarial prompt attack paradigm that subverts model judgments by injecting spurious decision criteria without altering the high-level task goal. |
| Outcome: | The proposed model subverts model judgments by injecting spurious decision criteria without altering the high-level task goal. |
Rethinking Reasoning: A Survey on Reasoning-based Backdoors in LLMs (2026.findings-acl)
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| Challenge: | Recent models such as OpenAI o1 and DeepSeek-R1 produce explicit reasoning traces, often via Chain-of-Thought prompting. |
| Approach: | They propose a taxonomy that offers a unified perspective for summarizing existing approaches and categorizing reasoning-based backdoor attacks into associative, passive, and active. |
| Outcome: | The proposed taxonomy categorizes reasoning-based backdoor attacks into associative, passive, and active. |
Reasoning Structure Matters for Safety Alignment of Reasoning Models (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. |
| Approach: | They propose a method that alters the reasoning structure of large reasoning models to achieve effective safety alignment by supervised fine-tuning. |
| Outcome: | The proposed method is practical and generalizable, requiring no complex training or reward design. |
Knowledge Conflicts for LLMs: A Survey (2024.emnlp-main)
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| Challenge: | This survey examines knowledge conflicts for large language models (LLMs) this survey aims to shed light on strategies for improving the robustness of LLMs . |
| Approach: | They focus on three categories of knowledge conflicts: context-memory, inter-context, and intra-membry conflict. |
| Outcome: | The findings highlight the challenges faced by large language models when blending contextual and parametric knowledge. |
FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models (2026.acl-long)
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| Challenge: | Recent Large Reasoning Models (LRMs) have demonstrated remarkable success in complex reasoning tasks. |
| Approach: | They propose a self-guided efficient reasoning framework that reduces FoE by pruning subs. |
| Outcome: | The proposed model outperforms eight competitive baselines while reducing token consumption by 37.7% 70.4%. |
SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities (2025.findings-acl)
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Fengqing Jiang, Zhangchen Xu, Yuetai Li, Luyao Niu, Zhen Xiang, Bo Li, Bill Yuchen Lin, Radha Poovendran
| Challenge: | Emerging large reasoning models (LRMs) leverage long chain-of-thought (CoT) reasoning to enhance their reasoning capabilities. |
| Approach: | They conduct a systematic study of LRM safety using human annotations to assess their safety. |
| Outcome: | The proposed safety measures are compared to state-of-the-art models on strong and wildjailbreak datasets. |