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Large Language Models (LLMs) have shown remarkable success in various tasks, yet their safety and the risk of generating harmful content remain pressing concerns.
Adversarial examples are not easily detected: Bypassing ten detection methods, 2017
Nicholas Carlini and David Wagner · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Glue: A multi-task benchmark and analysis platform for natural language understanding, 2019
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2019
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Language models are few-shot learners, 2020
Tom B. Brown et al · 2020
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts, 2020
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV au2, Eric Wallace, and Sameer Singh · 2020
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Constitutional ai: Harmlessness from ai feedback, 2022
Yuntao Bai et al · 2022
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Instruction induction: From few examples to natural language task descriptions, 2022
Or Honovich, Uri Shaham, Samuel R. Bowman, and Omer Levy · 2022
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Noisy channel language model prompting for few-shot text classification
Sewon Min, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2022
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Training language models to follow instructions with human feedback, 2022
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
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Red teaming language models with language models, 2022
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving · 2022
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Active example selection for in-context learning, 2022
Yiming Zhang, Shi Feng, and Chenhao Tan · 2022
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Detecting language model attacks with perplexity, 2023
Gabriel Alon and Michael Kamfonas · 2023
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Qwen technical report, 2023
Jinze Bai et al · 2023
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Defending against alignment-breaking attacks via robustly aligned llm, 2023
Bochuan Cao, Yuanpu Cao, Lu Lin, and Jinghui Chen · 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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Combating misinformation in the age of llms: Opportunities and challenges
Canyu Chen and Kai Shu · 2023
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Multilingual jailbreak challenges in large language models, 2023
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing · 2023
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A survey on in-context learning, 2023
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui · 2023
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Catastrophic jailbreak of open-source llms via exploiting generation, 2023
Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, and Danqi Chen · 2023
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Baseline defenses for adversarial attacks against aligned language models, 2023
Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, and Tom Goldstein · 2023
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Mistral 7b, 2023
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
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Pretraining language models with human preferences, 2023
Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Bhalerao, Christopher L. Buckley, Jason Phang, Samuel R. Bowman, and Ethan Perez · 2023
Cited alongside, same era.
Certifying llm safety against adversarial prompting, 2023
Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Soheil Feizi, and Hima Lakkaraju · 2023
Cited alongside, same era.
Open sesame! universal black box jailbreaking of large language models, 2023
Raz Lapid, Ron Langberg, and Moshe Sipper · 2023
Cited alongside, same era.
Deepinception: Hypnotize large language model to be jailbreaker, 2023
Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, and Bo Han · 2023
Cited alongside, same era.
Rain: Your language models can align themselves without finetuning, 2023
Yuhui Li, Fangyun Wei, Jinjing Zhao, Chao Zhang, and Hongyang Zhang · 2023
Cited alongside, same era.
Autodan: Generating stealthy jailbreak prompts on aligned large language models, 2023
Autodan: Interpretable gradient-based adversarial attacks on large language models, 2023
Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, and Tong Sun · 2023
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Universal and transferable adversarial attacks on aligned language models, 2023
Andy Zou, Zifan Wang, J. Zico Kolter, and Matt Fredrikson · 2023
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Jailbreaking leading safety-aligned llms with simple adaptive attacks
Maksym Andriushchenko, Francesco Croce, and Nicolas Flammarion · 2024
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Foundational challenges in assuring alignment and safety of large language models
Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, et al · 2024
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Red-teaming for generative ai: Silver bullet or security theater?
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Xiaogeng Liu, Nan Xu, Muhao Chen, and Chaowei Xiao · 2023
Cited alongside, same era.
Jailbreaking chatgpt via prompt engineering: An empirical study, 2023
Yi Liu, Gelei Deng, Zhengzi Xu, Yuekang Li, Yaowen Zheng, Ying Zhang, Lida Zhao, Tianwei Zhang, and Yang Liu · 2023
Cited alongside, same era.
Llm self defense: By self examination, llms know they are being tricked, 2023
Mansi Phute, Alec Helbling, Matthew Hull, ShengYun Peng, Sebastian Szyller, Cory Cornelius, and Duen Horng Chau · 2023
Cited alongside, same era.
"do anything now": Characterizing and evaluating in-the-wild jailbreak prompts on large language models, 2023
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron et al · 2023
Cited alongside, same era.
The art of defending: A systematic evaluation and analysis of llm defense strategies on safety and over-defensiveness, 2023
Neeraj Varshney, Pavel Dolin, Agastya Seth, and Chitta Baral · 2023
Cited alongside, same era.
Large language models are implicitly topic models: Explaining and finding good demonstrations for in-context learning
Xinyi Wang, Wanrong Zhu, Michael Saxon, Mark Steyvers, and William Yang Wang · 2023
Cited alongside, same era.
Michael Feffer, Anusha Sinha, Zachary C Lipton, and Hoda Heidari · 2024
Closest in time.
Aligner: Achieving efficient alignment through weak-to-strong correction, 2024
Jiaming Ji, Boyuan Chen, Hantao Lou, Donghai Hong, Borong Zhang, Xuehai Pan, Juntao Dai, and Yaodong Yang · 2024
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Mixtral of experts, 2024
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2024
Closest in time.
Artprompt: Ascii art-based jailbreak attacks against aligned llms
Fengqing Jiang, Zhangchen Xu, Luyao Niu, Zhen Xiang, Bhaskar Ramasubramanian, Bo Li, and Radha Poovendran · 2024
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Amplegcg: Learning a universal and transferable generative model of adversarial suffixes for jailbreaking both open and closed llms, 2024
Zeyi Liao and Huan Sun · 2024
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Harmbench: A standardized evaluation framework for automated red teaming and robust refusal
Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, David Forsyth, and Dan Hendrycks · 2024
Closest in time.
Tree of attacks: Jailbreaking black-box llms automatically, 2024
Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, and Amin Karbasi · 2024
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Gpt-4 technical report, 2024
OpenAI · 2024
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Visual adversarial examples jailbreak aligned large language models
Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Peter Henderson, Mengdi Wang, and Prateek Mittal · 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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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
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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 · 2024
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How johnny can persuade llms to jailbreak them: Rethinking persuasion to challenge ai safety by humanizing llms, 2024
Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, and Weiyan Shi · 2024
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Boosting jailbreak attack with momentum
Yihao Zhang and Zeming Wei · 2024
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Towards general conceptual model editing via adversarial representation engineering
Yihao Zhang, Zeming Wei, Jun Sun, and Meng Sun · 2024
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Accelerating greedy coordinate gradient via probe sampling
Yiran Zhao, Wenyue Zheng, Tianle Cai, Xuan Long Do, Kenji Kawaguchi, Anirudh Goyal, and Michael Shieh · 2024
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