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Although safely enhanced Large Language Models (LLMs) have achieved remarkable success in tackling various complex tasks in a zero-shot manner, they remain susceptible to jailbreak attacks, particularly the unknown jailbreak attack.
Combining belief functions based on distance of evidence
Deng Yong, Shi WenKang, Zhu ZhenFu, and Liu Qi · 2004
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Upper and lower probabilities induced by a multivalued mapping
Arthur P Dempster · 2008
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Yong Deng · 2016
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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GPTFUZZER: red teaming large language models with auto-generated jailbreak prompts
Jiahao Yu, Xingwei Lin, Zheng Yu, and Xinyu Xing · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J. Zico Kolter, and Matt Fredrikson · 2023
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Smoothllm: Defending large language models against jailbreaking attacks
Alexander Robey, Eric Wong, Hamed Hassani, and George J. Pappas · 2023
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Defending chatgpt against jailbreak attack via self-reminders
Yueqi Xie, Jingwei Yi, Jiawei Shao, Justin Curl, Lingjuan Lyu, Qifeng Chen, Xing Xie, and Fangzhao Wu · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Understanding hidden context in preference learning: Consequences for rlhf
Anand Siththaranjan, Cassidy Laidlaw, and Dylan Hadfield-Menell · 2023
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Analyzing the inherent response tendency of llms: Real-world instructions-driven jailbreak
Yanrui Du, Sendong Zhao, Ming Ma, Yuhan Chen, and Bing Qin · 2023
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Jailbreaking black box large language models in twenty queries, 2023
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, and Eric Wong · 2023
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Catastrophic jailbreak of open-source llms via exploiting generation
Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, and Danqi Chen · 2023
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Tree of attacks: Jailbreaking black-box llms automatically
Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, and Amin Karbasi · 2023
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Large language model unlearning
Yao Yuanshun, Xu Xiaojun, and Liu Yang · 2023
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Gpt-4 is too smart to be safe: Stealthy chat with llms via cipher
Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Pinjia He, Shuming Shi, and Zhaopeng Tu · 2023
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Multilingual jailbreak challenges in large language models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing · 2023
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Jailbreak in pieces: Compositional adversarial attacks on multi-modal language models
Erfan Shayegani, Yue Dong, and Nael Abu-Ghazaleh · 2023
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Decodingtrust: A comprehensive assessment of trustworthiness in GPT models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, and Bo Li · 2023
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Rain: Your language models can align themselves without finetuning
Yuhui Li, Fangyun Wei, Jinjing Zhao, Chao Zhang, and Hongyang Zhang · 2023
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Weak-to-strong extrapolation expedites alignment
Chujie Zheng, Ziqi Wang, Heng Ji, Minlie Huang, and Nanyun Peng · 2024
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Jailbreaklens: Visual analysis of jailbreak attacks against large language models
Yingchaojie Feng, Zhizhang Chen, Zhining Kang, Sijia Wang, Minfeng Zhu, Wei Zhang, and Wei Chen · 2024
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Attacking large language models with projected gradient descent
Simon Geisler, Tom Wollschläger, MHI Abdalla, Johannes Gasteiger, and Stephan Günnemann · 2024
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Improved techniques for optimization-based jailbreaking on large language models
Xiaojun Jia, Tianyu Pang, Chao Du, Yihao Huang, Jindong Gu, Yang Liu, Xiaochun Cao, and Min Lin · 2024
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C-rag: Certified generation risks for retrieval-augmented language models
Mintong Kang, Nezihe Merve Gürel, Ning Yu, Dawn Song, and Bo Li · 2024
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The instruction hierarchy: Training llms to prioritize privileged instructions
Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel · 2024
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Defending llms against jailbreaking attacks via backtranslation
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Mark Russinovich, Ahmed Salem, and Ronen Eldan · 2024
Cited alongside, same era.
Gradsafe: Detecting unsafe prompts for llms via safety-critical gradient analysis
Yueqi Xie, Minghong Fang, Renjie Pi, and Neil Gong · 2024
Cited alongside, same era.
On prompt-driven safeguarding for large language models
Chujie Zheng, Fan Yin, Hao Zhou, Fandong Meng, Jie Zhou, Kai-Wei Chang, Minlie Huang, and Nanyun Peng · 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
Cited alongside, same era.
Jailbreaking leading safety-aligned llms with simple adaptive attacks
Maksym Andriushchenko, Francesco Croce, and Nicolas Flammarion · 2024
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“Do Anything Now”: Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang · 2024
Cited alongside, same era.
Comprehensive assessment of jailbreak attacks against llms
Junjie Chu, Yugeng Liu, Ziqing Yang, Xinyue Shen, Michael Backes, and Yang Zhang · 2024
Cited alongside, same era.
Robust prompt optimization for defending language models against jailbreaking attacks
Andy Zhou, Bo Li, and Haohan Wang · 2024
Cited alongside, same era.
Yihan Wang, Zhouxing Shi, Andrew Bai, and Cho-Jui Hsieh · 2024
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Protecting your llms with information bottleneck
Zichuan Liu, Zefan Wang, Linjie Xu, Jinyu Wang, Lei Song, Tianchun Wang, Chunlin Chen, Wei Cheng, and Jiang Bian · 2024
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Exploring safety generalization challenges of large language models via code
Qibing Ren, Chang Gao, Jing Shao, Junchi Yan, Xin Tan, Wai Lam, and Lizhuang Ma · 2024
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Bells: A framework towards future proof benchmarks for the evaluation of llm safeguards
Diego Dorn, Alexandre Variengien, Charbel-Raphaël Segerie, and Vincent Corruble · 2024
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Autodefense: Multi-agent llm defense against jailbreak attacks
Yifan Zeng, Yiran Wu, Xiao Zhang, Huazheng Wang, and Qingyun Wu · 2024
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Xiaomeng Hu, Pin-Yu Chen, and Tsung-Yi Ho · 2024
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Defending large language models against jailbreak attacks via semantic smoothing
Jiabao Ji, Bairu Hou, Alexander Robey, George J Pappas, Hamed Hassani, Yang Zhang, Eric Wong, and Shiyu Chang · 2024
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Is the system message really important to jailbreaks in large language models?
Xiaotian Zou, Yongkang Chen, and Ke Li · 2024
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Mitigating fine-tuning jailbreak attack with backdoor enhanced alignment
Jiongxiao Wang, Jiazhao Li, Yiquan Li, Xiangyu Qi, Muhao Chen, Junjie Hu, Yixuan Li, Bo Li, and Chaowei Xiao · 2024
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Prompt-driven llm safeguarding via directed representation optimization
Chujie Zheng, Fan Yin, Hao Zhou, Fandong Meng, Jie Zhou, Kai-Wei Chang, Minlie Huang, and Nanyun Peng · 2024
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Pruning for protection: Increasing jailbreak resistance in aligned llms without fine-tuning
Adib Hasan, Ileana Rugina, and Alex Wang · 2024
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Efficient adversarial training in llms with continuous attacks
Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, Gauthier Gidel, and Leo Schwinn · 2024
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Cross-task defense: Instruction-tuning llms for content safety
Yu Fu, Wen Xiao, Jia Chen, Jiachen Li, Evangelos Papalexakis, Aichi Chien, and Yue Dong · 2024
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How far can camels go? exploring the state of instruction tuning on open resources
Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Chandu, David Wadden, Kelsey MacMillan, Noah A Smith, Iz Beltagy, et al · 2024
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