Root Defense Strategies: Ensuring Safety of LLM at the Decoding Level (2025.acl-long)
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| Challenge: | Existing methods to detect harmful outputs from prefill-level lacks utilization of the model’s decoding outputs, leading to relatively lower effectiveness and robustness. |
| Approach: | They propose a robust decoding mechanism that corrects harmful queries directly rather than rejecting them outright. |
| Outcome: | The proposed model improves model security without compromising reasoning speed. |
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| Challenge: | Large language models (LLMs) are susceptible to a type of attack known as jailbreaking, which misleads LLMs to output harmful contents. |
| Approach: | They propose to leverage hidden representations into existing jailbreak targets to move the attacks along the acceptance direction. |
| Outcome: | The proposed methods are validated using the objective of existing jailbreak attacks. |
SecDecoding: Steerable Decoding for Safer LLM Generation (2025.findings-emnlp)
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| Challenge: | Existing decoding-time defense methods suffer from limited generalization, high computational overhead, or significant utility degradation. |
| Approach: | They propose a decoding-time defense framework that leverages a pair of small contrastive models to estimate token-level safety signals by measuring divergence in their output distributions. |
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Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)
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Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang
| Challenge: | Large Language Models have been developed to deal with real-world crimes, but it remains unclear whether they internalize authentic knowledge or are forced to simulate toxic language patterns. |
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Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective (2025.coling-main)
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Tianlong Li, Zhenghua Wang, Wenhao Liu, Muling Wu, Shihan Dou, Changze Lv, Xiaohua Wang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Recent surge in jailbreaking attacks has revealed significant vulnerabilities in Large Language Models (LLMs) however, limited research into the underlying mechanisms that make LLMs vulnerable to such attacks has been conducted. |
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SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding (2024.acl-long)
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| Challenge: | Despite advances in large language models, they face substantial challenges in terms of safety. |
| Approach: | They develop a safety-aware decoding strategy for large language models to defend against jailbreak attacks. |
| Outcome: | The proposed strategy outperforms six defense methods against jailbreak attacks on five LLMs. |
Defending LLMs against Jailbreaking Attacks via Backtranslation (2024.findings-acl)
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| Challenge: | Recent advancement in large language models (LLMs) has shown their extensive applications and transformative potential to reshape people's lives. |
| Approach: | They propose a method which uses backtranslation to infer an input prompt from an input input prompt and then run it again on the backtranslated prompt. |
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SAFER: A Controllable Safeguard for LLMs against Backdoor Attacks (2026.findings-acl)
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| Challenge: | Existing inference-time defenses lack explicit control over false acceptance rate (FAR) existing inference time defenses aim to mitigate poisoned inputs but lack explicit FAR control . |
| Approach: | They propose a framework that provides explicit control over false acceptance rate without prior knowledge of backdoor samples. |
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CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations (2025.findings-acl)
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| Challenge: | Existing studies have isolated LLM jailbreak attacks and defenses . a new framework combines attack and defense to protect against malicious queries . |
| Approach: | They propose a framework that combines attack and defense to protect the Large Language Model (LLM) by embedding harmful problems into the safe area. |
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Beyond Surface Alignment: Rebuilding LLMs Safety Mechanism via Probabilistically Ablating Refusal Direction (2025.findings-emnlp)
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| Challenge: | Jailbreak attacks pose persistent threats to large language models . current safety alignment methods have insufficient safety alignment depth and unrobust internal defense mechanisms. |
| Approach: | a new safety alignment framework is developed to overcome jailbreak attacks . the framework forces the model to dynamically rebuild its refusal mechanisms from jailbreak states . |
| Outcome: | a new safety alignment framework reduces attack success rates by approximately 95% on four open-source LLM families and six representative attacks. |
From Attack Surfaces to Actual Operations: A Survey of Modern LLM Jailbreaks (2026.findings-acl)
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| Challenge: | Existing taxonomies focus on manipulation methods rather than underlying mechanisms, limiting our understanding of attack effectiveness and defensive strategies. |
| Approach: | They propose a two-fold taxonomy to categorize attacks across three tiers based on exploited vulnerabilities and approaches and an operational taxonomies to evaluate attacks across four dimensions. |
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