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Jailbreaking large language models (LLMs) has emerged as a critical security challenge with the widespread deployment of conversational AI systems.
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Together Computer. 2022 · 2022
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Improving alignment of dialogue agents via targeted human judgements
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Training language models to follow instructions with human feedback
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Mistral 7B Instruct v0.1
Mistral AI. 2023 · 2023
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Jailbreaker in Jail: Moving Target Defense for Large Language Models
Bocheng Chen, Advait Paliwal, and Qiben Yan. 2023 · 2023
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Mistral 7B
Albert Q. Jiang, Alexandre Sablayrolles, et al · 2023
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Aligning Large Language Models with Human: A Survey
Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, and Qun Liu. 2023 · 2023
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Jailbroken: How Does LLM Safety Training Fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2023 · 2023
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Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models
Xianjun Yang, Xiao Wang, Qi Zhang, Linda Petzold, William Yang Wang, Xun Zhao, and Dahua Lin. 2023 · 2023
Cited alongside, same era.
PoisonPrompt: Backdoor Attack on Prompt-based Large Language Models
Hongwei Yao, Jian Lou, and Zhan Qin. 2023 · 2023
Cited alongside, same era.
Gptfuzzer: Red teaming large language models with auto-generated jailbreak prompts
Jiahao Yu, Xingwei Lin, Zheng Yu, and Xinyu Xing. 2023 · 2023
Cited alongside, same era.
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al · 2023
Cited alongside, same era.
Universal and Transferable Adversarial Attacks on Aligned Language Models
“Do Anything Now”: Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models. In ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. 2024 · 2024
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Skipping Computations in Multimodal LLMs. In Workshop on Responsibly Building the Next Generation of Multimodal Foundational Models
Mustafa Shukor and Matthieu Cord. 2024 · 2024
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ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (Eds.). Association for Computational Linguistics, Miami, Florida, USA, 2697–2711
Hao Wang, Hao Li, Minlie Huang, and Lei Sha. 2024a · 2024
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Defending llms against jailbreaking attacks via backtranslation
Yihan Wang, Zhouxing Shi, Andrew Bai, and Cho-Jui Hsieh. 2024b · 2024
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Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. 2023 · 2023
Cited alongside, same era.
Jailbreaking Black Box Large Language Models in Twenty Queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, and Eric Wong. 2024 · 2024
Cited alongside, same era.
Multilingual Jailbreak Challenges in Large Language Models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing. 2024 · 2024
Cited alongside, same era.
Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators
Yann Dubois, Aarohi Srivastava, Abhinav Venigalla, et al · 2024
Cited alongside, same era.
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, 12622–12642
Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Anas Mahmoud, Bilge Acun, Saurabh Agarwal, Ahmed Roman, Ahmed Aly, Beidi Chen, and Carole-Jean Wu. 2024 · 2024
Cited alongside, same era.
WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models
Liwei Jiang, Kavel Rao, Seungju Han, Allyson Ettinger, Faeze Brahman, Sachin Kumar, Niloofar Mireshghallah, Ximing Lu, Maarten Sap, Yejin Choi, and Nouha Dziri. 2024 · 2024
Cited alongside, same era.
LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
Haitao Li, Qian Dong, Junjie Chen, Huixue Su, Yujia Zhou, Qingyao Ai, Ziyi Ye, and Yiqun Liu. 2024 · 2024
Cited alongside, same era.
AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models
Xiaogeng Liu, Nan Xu, Muhao Chen, and Chaowei Xiao. 2024 · 2024
Cited alongside, same era.
Later among the works it cites.
AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks
Yifan Zeng, Yiran Wu, Xiao Zhang, Huazheng Wang, and Qingyun Wu. 2024 · 2024
Later among the works it cites.
WordGame: Efficient & Effective LLM Jailbreak via Simultaneous Obfuscation in Query and Response
Tianrong Zhang, Bochuan Cao, Yuanpu Cao, Lu Lin, Prasenjit Mitra, and Jinghui Chen. 2024 · 2024
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Single-pass Detection of Jailbreaking Input in Large Language Models
Leyla Naz Candogan, Yongtao Wu, Elias Abad Rocamora, Grigorios Chrysos, and Volkan Cevher. 2025 · 2025
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CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM ’25) . ACM, 4857–4861
Sri Durga Sai Sowmya Kadali and Evangelos Papalexakis. 2025 · 2025
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Learning to Skip the Middle Layers of Transformers
Tim Lawson and Laurence Aitchison. 2025 · 2025
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Adaptive layer-skipping in pre-trained llms
Xuan Luo, Weizhi Wang, and Xifeng Yan. 2025 · 2025
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SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner
Xunguang Wang, Daoyuan Wu, Zhenlan Ji, Zongjie Li, Pingchuan Ma, Shuai Wang, Yingjiu Li, Yang Liu, Ning Liu, and Juergen Rahmel. 2025 · 2025
Later among the works it cites.
Defensive Prompt Patch: A Robust and Interpretable Defense of LLMs against Jailbreak Attacks
Chen Xiong, Xiangyu Qi, Pin-Yu Chen, and Tsung-Yi Ho. 2025 · 2025
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Comparative Analysis of LLM Abliteration Methods: A Cross-Architecture Evaluation
Richard J. Young. 2026 · 2026
Closest in time.
Instruction tuning for large language models: A survey
Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Guoyin Wang, et al · 2026
Closest in time.