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Large language models (LLMs) have exhibited outstanding performance in engaging with humans and addressing complex questions by leveraging their vast implicit knowledge and robust reasoning capabilities.
Gradient-based adversarial attacks against text transformers
Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, and Douwe Kiela · 2021
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Red-teaming large language models using chain of utterances for safety-alignment
Rishabh Bhardwaj and Soujanya Poria · 2023
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Jailbreaking black box large language models in twenty queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong · 2023
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Figstep: Jailbreaking large vision-language models via typographic visual prompts
Yichen Gong, Delong Ran, Jinyuan Liu, Conglei Wang, Tianshuo Cong, Anyu Wang, Sisi Duan, and Xiaoyun Wang · 2023
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Who is chatgpt? benchmarking llms’ psychological portrayal using psychobench
Jen-tse Huang, Wenxuan Wang, Eric John Li, Man Ho Lam, Shujie Ren, Youliang Yuan, Wenxiang Jiao, Zhaopeng Tu, and Michael R Lyu · 2023
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Llama guard: Llm-based input-output safeguard for human-ai conversations
Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, and Madian Khabsa · 2023
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Beavertails: Towards improved safety alignment of LLM via a human-preference dataset
Jiaming Ji, Mickel Liu, Josef Dai, Xuehai Pan, Chi Zhang, Ce Bian, Boyuan Chen, Ruiyang Sun, Yizhou Wang, and Yaodong Yang · 2023
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Automatically auditing large language models via discrete optimization
Erik Jones, Anca D. Dragan, Aditi Raghunathan, and Jacob Steinhardt · 2023
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Deepinception: Hypnotize large language model to be jailbreaker
Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, and Bo Han · 2023
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Exploiting large language models (llms) through deception techniques and persuasion principles
Sonali Singh, Faranak Abri, and Akbar Siami Namin · 2023
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Safety assessment of chinese large language models
Hao Sun, Zhexin Zhang, Jiawen Deng, Jiale Cheng, and Minlie Huang · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Do-not-answer: A dataset for evaluating safeguards in llms
Yuxia Wang, Haonan Li, Xudong Han, Preslav Nakov, and Timothy Baldwin · 2023
Cited alongside, same era.
Cvalues: Measuring the values of chinese large language models from safety to responsibility
Guohai Xu, Jiayi Liu, Ming Yan, Haotian Xu, Jinghui Si, Zhuoran Zhou, Peng Yi, Xing Gao, Jitao Sang, Rong Zhang, et al · 2023
Cited alongside, same era.
Low-resource languages jailbreak gpt-4
Zheng-Xin Yong, Cristina Menghini, and Stephen H Bach · 2023
Cited alongside, same era.
Safetybench: Evaluating the safety of large language models with multiple choice questions
Zhexin Zhang, Leqi Lei, Lindong Wu, Rui Sun, Yongkang Huang, Chong Long, Xiao Liu, Xuanyu Lei, Jie Tang, and Minlie Huang · 2023
Cited alongside, same era.
Fine-tuning aligned language models compromises safety, even when users do not intend to!
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al · 2024
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Great, now write an article about that: The crescendo multi-turn llm jailbreak attack
Mark Russinovich, Ahmed Salem, and Ronen Eldan · 2024
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The language barrier: Dissecting safety challenges of llms in multilingual contexts
Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, and Daniel Khashabi · 2024
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Sandwich attack: Multi-language mixture adaptive attack on llms
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Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J Zico Kolter, and Matt Fredrikson · 2023
Cited alongside, same era.
Safety-tuned llamas: Lessons from improving the safety of large language models that follow instructions
Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, Paul Röttger, Dan Jurafsky, Tatsunori Hashimoto, and James Zou · 2024
Cited alongside, same era.
Self-evaluation as a defense against adversarial attacks on llms
Hannah Brown, Leon Lin, Kenji Kawaguchi, and Michael Shieh · 2024
Cited alongside, same era.
Emerging vulnerabilities in frontier models: Multi-turn jailbreak attacks
Tom Gibbs, Ethan Kosak-Hine, George Ingebretsen, Jason Zhang, Julius Broomfield, Sara Pieri, Reihaneh Iranmanesh, Reihaneh Rabbany, and Kellin Pelrine · 2024
Cited alongside, same era.
Pku-saferlhf: A safety alignment preference dataset for llama family models
Jiaming Ji, Donghai Hong, Borong Zhang, Boyuan Chen, Josef Dai, Boren Zheng, Tianyi Qiu, Boxun Li, and Yaodong Yang · 2024
Cited alongside, same era.
Red queen: Safeguarding large language models against concealed multi-turn jailbreaking
Yifan Jiang, Kriti Aggarwal, Tanmay Laud, Kashif Munir, Jay Pujara, and Subhabrata Mukherjee · 2024
Cited alongside, same era.
Exploiting programmatic behavior of llms: Dual-use through standard security attacks
Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, and Tatsunori Hashimoto · 2024
Cited alongside, same era.
Salad-bench: A hierarchical and comprehensive safety benchmark for large language models
Lijun Li, Bowen Dong, Ruohui Wang, Xuhao Hu, Wangmeng Zuo, Dahua Lin, Yu Qiao, and Jing Shao · 2024
Cited alongside, same era.
Bibek Upadhayay and Vahid Behzadan · 2024
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SELF-GUARD: empower the LLM to safeguard itself
Zezhong Wang, Fangkai Yang, Lu Wang, Pu Zhao, Hongru Wang, Liang Chen, Qingwei Lin, and Kam-Fai Wong · 2024
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Evaluating implicit bias in large language models by attacking from a psychometric perspective
Yuchen Wen, Keping Bi, Wei Chen, Jiafeng Guo, and Xueqi Cheng · 2024
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Cognitive overload: Jailbreaking large language models with overloaded logical thinking
Nan Xu, Fei Wang, Ben Zhou, Bangzheng Li, Chaowei Xiao, and Muhao Chen · 2024
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Chain of attack: a semantic-driven contextual multi-turn attacker for llm
Xikang Yang, Xuehai Tang, Songlin Hu, and Jizhong Han · 2024
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How johnny can persuade llms to jailbreak them: Rethinking persuasion to challenge AI safety by humanizing llms
Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, and Weiyan Shi · 2024
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Parden, can you repeat that? defending against jailbreaks via repetition
Ziyang Zhang, Qizhen Zhang, and Jakob Nicolaus Foerster · 2024
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Safety fine-tuning at (almost) no cost: A baseline for vision large language models
Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, Yongxin Yang, and Timothy M. Hospedales · 2024
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