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Large language models (LLMs) have been widely deployed as the backbone with additional tools and text information for real-world applications.
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 · 1901
Earlier work this paper cites.
Keying hash functions for message authentication
Mihir Bellare, Ran Canetti, and Hugo Krawczyk. 1996 · 1996
Earlier work this paper cites.
HMAC: Keyed-Hashing for Message Authentication
Dr. Hugo Krawczyk, Mihir Bellare, and Ran Canetti. 1997 · 1997
Earlier work this paper cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner. 2018 · 2018
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
Ignore previous prompt: Attack techniques for language models
Fábio Perez and Ian Ribeiro. 2022 · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Earlier work this paper cites.
React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. 2022 · 2022
Earlier work this paper cites.
Improving image generation with better captions
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. 2023 · 2023
Earlier work this paper cites.
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
Earlier work this paper cites.
Securing llm systems against prompt injection
Rich Harang. 2023 · 2023
Cited alongside, same era.
LangChain
LangChain. 2023 · 2023
Cited alongside, same era.
New Bing
Microsoft. 2023 · 2023
Cited alongside, same era.
OWASP Top 10 for LLM Applications
OWASP. 2023 · 2023
Cited alongside, same era.
Gorilla: Large language model connected with massive apis
Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. 2023 · 2023
Cited alongside, same era.
Toolllm: Facilitating large language models to master 16000+ real-world apis
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. 2023 · 2023
Cited alongside, same era.
Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023 · 2023
Later among the works it cites.
Llama 3 model card
AI@Meta. 2024 · 2024
Closest in time.
Struq: Defending against prompt injection with structured queries
Sizhe Chen, Julien Piet, Chawin Sitawarin, and David Wagner. 2024 · 2024
Closest in time.
Mind2web: Towards a generalist agent for the web
Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, and Yu Su. 2024 · 2024
Closest in time.
Defending against indirect prompt injection attacks with spotlighting
Keegan Hines, Gary Lopez, Matthew Hall, Federico Zarfati, Yonatan Zunger, and Emre Kiciman. 2024 · 2024
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
Cited alongside, same era.
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. 2023 · 2023
Cited alongside, same era.
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 · 2023
Cited alongside, same era.
Assessing prompt injection risks in 200+ custom gpts
Jiahao Yu, Yuhang Wu, Dong Shu, Mingyu Jin, and Xinyu Xing. 2023 · 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. 2023a
Cited in the paper.
Prompt injection attacks and defenses in llm-integrated applications
Yupei Liu, Yuqi Jia, Runpeng Geng, Jinyuan Jia, and Neil Zhenqiang Gong. 2023b
Cited in the paper.
Closest in time.
Automatic and universal prompt injection attacks against large language models
Xiaogeng Liu, Zhiyuan Yu, Yizhe Zhang, Ning Zhang, and Chaowei Xiao. 2024 · 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 · 2024
Closest in time.
Injecagent: Benchmarking indirect prompt injections in tool-integrated large language model agents
Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. 2024 · 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 · 2024
Closest in time.