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Large Language Models (LLMs) are deployed in interactive contexts with direct user engagement, such as chatbots and writing assistants.
Software vulnerabilities: full-, responsible-, and non-disclosure
Andrew Cencini, Kevin Yu, and Tony Chan. 2005 · 2005
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Distributed Denial of Service Attack and Defense
Shui Yu. 2013 · 2013
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum. 2015 · 2015
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, and Kate Crawford. 2018 · 2018
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Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples
Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. 2019 · 2019
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Beat the AI: Investigating adversarial human annotation for reading comprehension
Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp. 2020 · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020
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Extracting Training Data from Large Language Models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Xiaodong Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2020 · 2020
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RealToxicityPrompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020 · 2020
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Best practices and recommendations for cybersecurity service providers
Alexey Kirichenko, Markus Christen, Florian Grunow, and Dominik Herrmann. 2020 · 2020
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Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
Marco Tulio Ribeiro, Tongshuang Sherry Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
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A Survey of Privacy Attacks in Machine Learning
Maria Rigaki and Sebastian Garcia. 2020 · 2020
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, S. Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen A. Creel, Jared Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren E. Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas F. Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, O. Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Benjamin Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, J. F. Nyarko, Giray Ogut, Laurel J. Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Robert Reich, Hongyu Ren, Frieda Rong, Yusuf H. Roohani, Camilo Ruiz, Jack Ryan, Christopher R’e, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishna Parasuram Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei A. Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, and Percy Liang. 2021 · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 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 · 2022
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Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned
Deep Ganguli, Liane Lovitt, John Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Benjamin Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy Jones, Sam Bowman, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El-Showk, Stanislav Fort, Zachary Dodds, T. J. Henighan, Danny Hernandez, Tristan Hume, Josh Jacobson, Scott Johnston, Shauna Kravec, Catherine Olsson, Sam Ringer, Eli Tran-Johnson, Dario Amodei, Tom B. Brown, Nicholas Joseph, Sam McCandlish, Christopher Olah, Jared Kaplan, and Jack Clark. 2022 · 2022
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Ehud Karpas, Omri Abend, Yonatan Belinkov, Barak Lenz, Opher Lieber, Nir Ratner, Yoav Shoham, Hofit Bata, Yoav Levine, Kevin Leyton-Brown, Dor Muhlgay, Noam Rozen, Erez Schwartz, Gal Shachaf, Shai Shalev-Shwartz, Amnon Shashua, and Moshe Tenenholtz. 2022 · 2022
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Prompt waywardness: The curious case of discretized interpretation of continuous prompts
Daniel Khashabi, Xinxi Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, Sameer Singh, and Yejin Choi. 2022 · 2022
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Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models
Robert L. Logan, Ivana Balažević, Eric Wallace, Fabio Petroni, Sameer Singh, and Sebastian Riedel. 2021 · 2022
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Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Training language models to follow instructions with human feedback
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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Red teaming language models with language models
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nathan McAleese, and Geoffrey Irving. 2022 · 2022
Cited alongside, same era.
Ignore Previous Prompt: Attack Techniques For Language Models
Fábio Perez and Ian Ribeiro. 2022 · 2022
Cited alongside, same era.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022
Cited alongside, same era.
Learn Prompting
Sander Schulhoff. 2022 · 2022
Cited alongside, same era.
Exploring prompt injection attacks
Jose Selvi. 2022 · 2022
Cited alongside, same era.
OpenAI. 2023 · 2023
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Visual Adversarial Examples Jailbreak Large Language Models
Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Mengdi Wang, and Prateek Mittal. 2023 · 2023
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Tricking LLMs into disobedience: Understanding, analyzing, and preventing jailbreaks
Abhinav Rao, Sachin Vashistha, Atharva Naik, Somak Aditya, and Monojit Choudhury. 2023 · 2023
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SolidGoldMagikarp (plus, prompt generation)
Jessica Rumbelow and mwatkins. 2023 · 2023
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On the Adversarial Robustness of Multi-Modal Foundation Models
Christian Schlarmann and Matthias Hein. 2023 · 2023
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An information-theoretic approach to prompt engineering without ground truth labels
Taylor Sorensen, Joshua Robinson, Christopher Rytting, Alexander Shaw, Kyle Rogers, Alexia Delorey, Mahmoud Khalil, Nancy Fulda, and David Wingate. 2022 · 2022
Cited alongside, same era.
Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick. 2022 · 2022
Cited alongside, same era.
Chain of Thought Prompting Elicits Reasoning in Large Language Models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Huai hsin Chi, F. Xia, Quoc Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
(Ab)using Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs
Eugene Bagdasaryan, Tsung-Yin Hsieh, Ben Nassi, and Vitaly Shmatikov. 2023 · 2023
Cited alongside, same era.
Ceci n’est pas une pomme: Adversarial Illusions in Multi-Modal Embeddings
Eugene Bagdasaryan and Vitaly Shmatikov. 2023 · 2023
Cited alongside, same era.
Are aligned neural networks adversarially aligned?
Nicholas Carlini, Milad Nasr, Christopher A Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramer, et al. 2023 · 2023
Cited alongside, same era.
How a $5000 Prompt Injection Contest Helped Me Become a Better Prompt Engineer
Christopher R. Carnahan. 2023 · 2023
Cited alongside, same era.
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On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning
Omar Shaikh, Hongxin Zhang, William Held, Michael Bernstein, and Diyi Yang. 2023 · 2023
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Plug and Pray: Exploiting off-the-shelf components of Multi-Modal Models
Erfan Shayegani, Yue Dong, and Nael Abu-Ghazaleh. 2023 · 2023
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Xinyu Shen, Zeyuan Johnson Chen, Michael Backes, Yun Shen, and Yang Zhang. 2023 · 2023
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Prompting GPT-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan L. Boyd-Graber, and Lijuan Wang. 2023 · 2023
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Achieving code execution in mathGPT via prompt injection
Ludwig-Ferdinand Stumpp. 2023 · 2023
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Hackaprompt 2023
Terjanq. 2023 · 2023
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New jailbreak based on virtual functions - smuggle illegal tokens to the backend
u/Nin_kat. 2023 · 2023
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Protect Your Prompts: Protocols for IP Protection in LLM Applications
M. A. van Wyk, M. Bekker, X. L. Richards, and K. J. Nixon. 2023 · 2023
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Prompting PaLM for Translation: Assessing Strategies and Performance
David Vilar, Markus Freitag, Colin Cherry, Jiaming Luo, Viresh Ratnakar, and George F. Foster. 2023 · 2023
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Jailbroken: How does LLM safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2023 · 2023
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The dual LLM pattern for building AI assistants that can resist prompt injection
Simon Willison. 2023 · 2023
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Defending ChatGPT against jailbreak attack via self-reminder
Yueqi Xie, Jingwei Yi, Jiawei Shao, Justin Curl, Lingjuan Lyu, Qifeng Chen, Xing Xie, and Fangzhao Wu. 2023 · 2023
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Low-Resource Languages Jailbreak GPT-4
Zheng-Xin Yong, Cristina Menghini, and Stephen H. Bach. 2023 · 2023
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Why johnny can’t prompt: How non-ai experts try (and fail) to design LLM prompts
J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, and Qian Yang. 2023 · 2023
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AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning
Ziqi Zhou, Shengshan Hu, Minghui Li, Hangtao Zhang, Yechao Zhang, and Hai Jin. 2023 · 2023
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PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Weirong Ye, Neil Zhenqiang Gong, Yue Zhang, and Xingxu Xie. 2023 · 2023
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Prompt injection vs jailbreaking: What is the difference?
Sander V Schulhoff. 2024 · 2024
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