Foot-In-The-Door: A Multi-turn Jailbreak for LLMs

Mart: Improving llm safety with multi-round automatic red-teaming

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Training a helpful and harmless assistant with reinforcement learning from human feedback

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Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned

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Jailbreaking black box large language models in twenty queries

Original

Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong. 2023 · 2023

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Attack prompt generation for red teaming and defending large language models

Boyi Deng, Wenjie Wang, Fuli Feng, Yang Deng, Qifan Wang, and Xiangnan He. 2023a · 2023

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Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection

Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. 2023 · 2023

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Llama guard: Llm-based input-output safeguard for human-ai conversations

Original

Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, et al. 2023 · 2023

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Mistral 7b

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Automatically auditing large language models via discrete optimization

Erik Jones, Anca Dragan, Aditi Raghunathan, and Jacob Steinhardt. 2023 · 2023

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Efficient memory management for large language model serving with pagedattention

Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023

Cited alongside, same era.

Deepinception: Hypnotize large language model to be jailbreaker

Original

Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, and Bo Han. 2023 · 2023

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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. 2023 · 2023

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Jailbreak and guard aligned language models with only few in-context demonstrations

Original

Zeming Wei, Yifei Wang, Ang Li, Yichuan Mo, and Yisen Wang. 2023 · 2023

Cited alongside, same era.

Universal and transferable adversarial attacks on aligned language models

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 A. Forsyth, and Dan Hendrycks. 2024 · 2024

Later among the works it cites.

Fight back against jailbreaking via prompt adversarial tuning

Yichuan Mo, Yuji Wang, Zeming Wei, and Yisen Wang. 2024 · 2024

Later among the works it cites.

Codeattack: Revealing safety generalization challenges of large language models via code completion

Qibing Ren, Chang Gao, Jing Shao, Junchi Yan, Xin Tan, Wai Lam, and Lizhuang Ma. 2024a · 2024

Later among the works it cites.

Codeattack: Revealing safety generalization challenges of large language models via code completion

Qibing Ren, Chang Gao, Jing Shao, Junchi Yan, Xin Tan, Wai Lam, and Lizhuang Ma. 2024b · 2024

Later among the works it cites.

Great, now write an article about that: The crescendo multi-turn llm jailbreak attack

Original

Mark Russinovich, Ahmed Salem, and Ronen Eldan. 2024 · 2024