Defensive Prompt Patch: A Robust and Interpretable Defense of LLMs against Jailbreak Attacks

Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai

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Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera. 2019 · 1910

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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 · 2022

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Rlprompt: Optimizing discrete text prompts with reinforcement learning

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Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, and Zhiting Hu. 2022 · 2022

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Training language models to follow instructions with human feedback

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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 · 2022

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Jailbreaking black box large language models in twenty queries

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

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Language models show human-like content effects on reasoning tasks

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Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, Hannah R. Sheahan, Antonia Creswell, Dharshan Kumaran, James L. McClelland, and Felix Hill. 2023 · 2023

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Catastrophic jailbreak of open-source llms via exploiting generation

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Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, and Danqi Chen. 2023 · 2023

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

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

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Baseline defenses for adversarial attacks against aligned language models

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Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, and Tom Goldstein. 2023 · 2023

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Mistral 7b

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Alpacaeval: An automatic evaluator of instruction-following models

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Autodan: Generating stealthy jailbreak prompts on aligned large language models

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Xiaogeng Liu, Nan Xu, Muhao Chen, and Chaowei Xiao. 2023 · 2023

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Tree of attacks: Jailbreaking black-box llms automatically

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Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, and Amin Karbasi. 2023 · 2023

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Agieval: A human-centric benchmark for evaluating foundation models

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Starling-7b: Improving llm helpfulness and harmlessness with rlaif

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Universal and transferable adversarial attacks on aligned language models

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Andy Zou, Zifan Wang, J. Zico Kolter, and Matt Fredrikson. 2023 · 2023

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Jailbreaking leading safety-aligned llms with simple adaptive attacks

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Maksym Andriushchenko, Francesco Croce, and Nicolas Flammarion. 2024 · 2024

Closest in time.

Jailbreakbench: An open robustness benchmark for jailbreaking large language models