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While Large Language Models (LLMs) have achieved tremendous success in various applications, they are also susceptible to jailbreaking attacks.
Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
Earlier work this paper cites.
Ms marco: A human generated machine reading comprehension dataset
Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, and Tong Wang · 2018
Earlier work this paper cites.
On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
Earlier work this paper cites.
Improving adversarial robustness requires revisiting misclassified examples
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu · 2020
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Skip connections matter: On the transferability of adversarial examples generated with resnets
Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey, and Xingjun Ma · 2020
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Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
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Unlearnable examples: Making personal data unexploitable
Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang · 2021
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Finding optimal tangent points for reducing distortions of hard-label attacks
Chen Ma, Xiangyu Guo, Li Chen, Jun-Hai Yong, and Yisen Wang · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan · 2022
Earlier work this paper cites.
Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
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A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui · 2022
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When adversarial training meets vision transformers: Recipes from training to architecture
Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, and Yisen Wang · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Earlier work this paper cites.
Ignore previous prompt: Attack techniques for language models
Fábio Perez and Ian Ribeiro · 2022
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Dan is my new friend
Walker Spider · 2022
Earlier work this paper cites.
Self-ensemble adversarial training for improved robustness
Hongjun Wang and Yisen Wang · 2022
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Gpt-4 technical report
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Earlier work this paper cites.
Detecting language model attacks with perplexity
Gabriel Alon and Michael Kamfonas · 2023
Earlier work this paper cites.
Query efficient black-box adversarial attack on deep neural networks
Yang Bai, Yisen Wang, Yuyuan Zeng, Yong Jiang, and Shu-Tao Xia · 2023
Earlier work this paper cites.
Red-teaming large language models using chain of utterances for safety-alignment
Rishabh Bhardwaj and Soujanya Poria · 2023
Earlier work this paper cites.
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 · 2023
Earlier work this paper cites.
The hacking of ChatGPT is just getting started
Matt Burgess · 2023
Cited alongside, same era.
Defending against alignment-breaking attacks via robustly aligned llm
Bochuan Cao, Yuanpu Cao, Lu Lin, and Jinghui Chen · 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 · 2023
Cited alongside, same era.
Amazing "jailbreak" bypasses ChatGPT’s ethics safeguards
Jon Christian · 2023
Cited alongside, same era.
Attack prompt generation for red teaming and defending large language models
Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang, and Xiangnan He · 2023
Cited alongside, same era.
Mathprompter: Mathematical reasoning using large language models
Shima Imani, Liang Du, and Harsh Shrivastava · 2023
Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J. Zico Kolter, and Matt Fredrikson · 2023
Later among the works it cites.
Multilingual jailbreak challenges in large language models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing · 2024
Closest in time.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
Closest in time.
Cold-attack: Jailbreaking llms with stealthiness and controllability
Xingang Guo, Fangxu Yu, Huan Zhang, Lianhui Qin, and Bin Hu · 2024
Closest in time.
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
Closest in time.
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Cited alongside, same era.
Baseline defenses for adversarial attacks against aligned language models
Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, and Tom Goldstein · 2023
Cited alongside, same era.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
Cited alongside, same era.
Certifying llm safety against adversarial prompting
Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Aaron Jiaxun Li, Soheil Feizi, and Himabindu Lakkaraju · 2023
Cited alongside, same era.
Multi-step jailbreaking privacy attacks on chatgpt
Haoran Li, Dadi Guo, Wei Fan, Mingshi Xu, Jie Huang, Fanpu Meng, and Yangqiu Song · 2023
Cited alongside, same era.
Improving large language model fine-tuning for solving math problems
Yixin Liu, Avi Singh, C Daniel Freeman, John D Co-Reyes, and Peter J Liu · 2023
Cited alongside, same era.
Tree of attacks: Jailbreaking black-box llms automatically
Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, and Amin Karbasi · 2023
Cited alongside, same era.
Artūrs Kanepajs, Vladimir Ivanov, and Richard Moulange · 2024
Closest in time.
A cross-language investigation into jailbreak attacks in large language models
Jie Li, Yi Liu, Chongyang Liu, Ling Shi, Xiaoning Ren, Yaowen Zheng, Yang Liu, and Yinxing Xue · 2024
Closest in time.
Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang · 2024
Closest in time.
Llm self defense: By self examination, llms know they are being tricked
Mansi Phute, Alec Helbling, Matthew Daniel Hull, ShengYun Peng, Sebastian Szyller, Cory Cornelius, and Duen Horng Chau · 2024
Closest in time.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
Closest in time.
" 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
Closest in time.
A theoretical understanding of self-correction through in-context alignment
Yifei Wang, Yuyang Wu, Zeming Wei, Stefanie Jegelka, and Yisen Wang · 2024
Closest in time.
On the adversarial transferability of generalized" skip connections"
Yisen Wang, Yichuan Mo, Dongxian Wu, Mingjie Li, Xingjun Ma, and Zhouchen Lin · 2024
Closest in time.
Safedecoding: Defending against jailbreak attacks via safety-aware decoding
Zhangchen Xu, Fengqing Jiang, Luyao Niu, Jinyuan Jia, Bill Yuchen Lin, and Radha Poovendran · 2024
Closest in time.
A survey on large language model (llm) security and privacy: The good, the bad, and the ugly
Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, and Yue Zhang · 2024
Closest in time.
On the duality between sharpness-aware minimization and adversarial training
Yihao Zhang, Hangzhou He, Jingyu Zhu, Huanran Chen, Yifei Wang, and Zeming Wei · 2024
Closest in time.
Towards general conceptual model editing via adversarial representation engineering
Yihao Zhang, Zeming Wei, Jun Sun, and Meng Sun · 2024
Closest in time.
Safe unlearning: A surprisingly effective and generalizable solution to defend against jailbreak attacks
Zhexin Zhang, Junxiao Yang, Pei Ke, Shiyao Cui, Chujie Zheng, Hongning Wang, and Minlie Huang · 2024
Closest in time.
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
Closest in time.
Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2024
Closest in time.
Virtual context: Enhancing jailbreak attacks with special token injection
Yuqi Zhou, Lin Lu, Hanchi Sun, Pan Zhou, and Lichao Sun · 2024
Closest in time.
Autodan: Interpretable gradient-based adversarial attacks on large language models
Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, and Tong Sun · 2024
Closest in time.
Is the system message really important to jailbreaks in large language models?
Xiaotian Zou, Yongkang Chen, and Ke Li · 2024
Closest in time.