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Today's LLMs are susceptible to prompt injections, jailbreaks, and other attacks that allow adversaries to overwrite a model's original instructions with their own malicious prompts.
Introduction and overview of the Multics system
Fernando J Corbató and Victor A Vyssotsky · 1965
Earlier work this paper cites.
The UNIX time-sharing system
Dennis M. Ritchie and Ken Thompson · 1974
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The essence of command injection attacks in web applications
Zhendong Su and Gary Wassermann · 2006
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On automated prepared statement generation to remove SQL injection vulnerabilities
Stephen Thomas, Laurie Williams, and Tao Xie · 2009
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Universal adversarial triggers for attacking and analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh · 2019
Earlier work this paper cites.
A general language assistant as a laboratory for alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al · 2021
Earlier work this paper cites.
WebGPT: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al · 2021
Earlier work this paper cites.
Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al · 2022
Earlier work this paper cites.
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 Gray, John Schulman, et al · 2022
Earlier work this paper cites.
TALM: Tool augmented language models
Aaron Parisi, Yao Zhao, and Noah Fiedel · 2022
Earlier work this paper cites.
Red teaming language models with language models
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving · 2022
Earlier work this paper cites.
Ignore previous prompt: Attack techniques for language models
Fábio Perez and Ian Ribeiro · 2022
Cited alongside, same era.
Learning by distilling context
Charlie Snell, Dan Klein, and Ruiqi Zhong · 2022
Cited alongside, same era.
Prompt injection attacks against GPT-3, 2022
Simon Willison · 2022
Cited alongside, same era.
Gemini: A family of highly capable multimodal models
Gemini, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Multi-modal prompt injection image attacks against GPT-4V, 2023
Simon Willison · 2023
Later among the works it cites.
Benchmarking and defending against indirect prompt injection attacks on large language models
Jingwei Yi, Yueqi Xie, Bin Zhu, Keegan Hines, Emre Kiciman, Guangzhong Sun, Xing Xie, and Fangzhao Wu · 2023
Later among the works it cites.
Prompts should not be seen as secrets: Systematically measuring prompt extraction attack success
Yiming Zhang and Daphne Ippolito · 2023
Later among the works it cites.
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.
StruQ: Defending against prompt injection with structured queries
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Gandalf game—Level 4 adventure, 2023
Lakera AI · 2023
Cited alongside, same era.
Prompt injection attack against LLM-integrated applications
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, and Yang Liu · 2023
Cited alongside, same era.
GPT-4 technical report, 2023
OpenAI · 2023
Cited alongside, same era.
Ignore this title and HackAPrompt: Exposing systemic vulnerabilities of llms through a global scale prompt hacking competition
Sander Schulhoff, Jeremy Pinto, Anaum Khan, Louis-François Bouchard, Chenglei Si, Svetlina Anati, Valen Tagliabue, Anson Liu Kost, Christopher Carnahan, and Jordan Boyd-Graber · 2023
Cited alongside, same era.
Jailbroken: How does LLM safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt · 2023
Cited alongside, same era.
LLM-powered autonomous agents
Lilian Weng · 2023
Cited alongside, same era.
Sizhe Chen, Julien Piet, Chawin Sitawarin, and David Wagner · 2024
Closest in time.
Coercing LLMs to do and reveal (almost) anything
Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, and Tom Goldstein · 2024
Closest in time.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2024
Closest in time.
HuggingGPT: Solving AI tasks with ChatGPT and its friends in hugging face
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang · 2024
Closest in time.
Tensor Trust: Interpretable prompt injection attacks from an online game
Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, et al · 2024
Closest in time.
Command injection — OWASP foundation, 2024
Weilin Zhong, Wichers, Amwestgate, Rezos, Clow808, KristenS, Jason Li, Andrew Smith, Jmanico, Tal Mel, and kingthorin · 2024
Closest in time.
Can LLMs separate instructions from data? And what do we even mean by that?
Egor Zverev, Sahar Abdelnabi, Mario Fritz, and Christoph H Lampert · 2024
Closest in time.