| Challenge: | Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications, but concerns regarding their security persist. |
| Approach: | They propose an adversarial game that leverages agent learning to extend knowledge to defend against jailbreaks. |
| Outcome: | The proposed game shows that LLMs safeguarded by ICAG exhibit significantly reduced jailbreak success rates across various attack scenarios. |
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Defending LLMs against Jailbreak Attacks via Template-Based ICL with a Defensive Suffix (2026.findings-acl)
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| Challenge: | State-of-the-art large language models (LLMs) are vulnerable to jailbreak attacks, such as GCG and AutoDAN. |
| Approach: | They propose to take the advances of online In-Context Learning and an offline defensive suffix and optimize them using an iterative algorithm and an online stochastic random search to identify the most effective ICL demonstrations. |
| Outcome: | The proposed method reduces attack success rate to nearly *0% while maintaining the model’s utility on benign tasks and incurring only *negligible* computational overhead. |
AGD: Adversarial Game Defense Against Jailbreak Attacks in Large Language Models (2025.acl-long)
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Shilong Pan, Zhiliang Tian, Zhen Huang, Wanlong Yu, Zhihua Wen, Xinwang Liu, Kai Lu, Minlie Huang, Dongsheng Li
| Challenge: | Existing defenses, including post-training alignment and prompt engineering, struggle with adaptability to out-of-distribution (OOD) attacks. |
| Approach: | They propose an adversarial game-based defense method that dynamically adjusts LLMs’ internal representations to achieve a balanced trade-off between helpfulness and harmlessness. |
| Outcome: | The proposed method improves LLMs’ safety over all baselines. |
Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities (2025.naacl-long)
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Chung-En Sun, Xiaodong Liu, Weiwei Yang, Tsui-Wei Weng, Hao Cheng, Aidan San, Michel Galley, Jianfeng Gao
| Challenge: | Recent research shows that Large Language Models (LLMs) are vulnerable to automated jailbreak attacks. |
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| Outcome: | ADV-LLM significantly reduces the computational cost of generating adversarial suffixes while achieving nearly 100% ASR on various open-source LLMs. |
Prompt Optimization via Adversarial In-Context Learning (2024.acl-long)
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Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
| Challenge: | Existing methods to optimize prompts for in-context learning are based on adversarial learning and are computationally efficient and extensible to other LLMs and tasks. |
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Exploring Jailbreak Attacks on LLMs through Intent Concealment and Diversion (2025.findings-acl)
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| Challenge: | Existing jailbreak methods face an excessive number of iterative queries and poor generalization across models. |
| Approach: | They propose a jailbreak method that employs **I**ntent **C**oncealment and div**E**rsion to circumvent security constraints. |
| Outcome: | The proposed method outperforms existing jailbreak techniques in question-answering and text-generation tasks. |
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. |
| Outcome: | The proposed framework achieves an average jailbreak success rate of 88.85% across three popular LLMs while the defense success rate reaches an average of 84.17%. |
Defensive Prompt Patch: A Robust and Generalizable Defense of Large Language Models against Jailbreak Attacks (2025.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have showcased their ability to understand and generate text akin to human interaction. |
| Approach: | They propose a prompt-based defense mechanism specifically designed to protect LLMs against jailbreak attacks by introducing jailbreak prompts into malicious queries. |
| Outcome: | Empirical results show that the proposed defense outperforms existing defense strategies in balancing safety and utility while maintaining high utility. |
Agents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks (2025.acl-long)
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| Challenge: | Multi-agent LLMs are prone to adversarial attacks because of constraints such as limited token bandwidth and latency between message delivery. |
| Approach: | They propose a permutation-invariant adversarial attack that optimizes prompt distribution across latency and bandwidth constraints to bypass distributed safety mechanisms within the system. |
| Outcome: | The proposed method outperforms conventional attacks by up to 7 on multiple models. |
Virtual Context Enhancing Jailbreak Attacks with Special Token Injection (2024.findings-emnlp)
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| Challenge: | Existing jailbreak attacks target the two phases of user interaction: prompt input and model computation. |
| Approach: | They propose a new tool that leverages special tokens to improve jailbreak attacks . they found that the tool can increase success rates of existing jailbreak methods by 40% . |
| Outcome: | The proposed solution can improve success rates of four widely used jailbreak methods by approximately 40% across various 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. |
| Outcome: | The proposed method outperforms baselines and has little impact on the generation quality for benign input prompts. |