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Although LLM-based agents, powered by Large Language Models (LLMs), can use external tools and memory mechanisms to solve complex real-world tasks, they may also introduce critical security vulnerabilities.
A survey of man in the middle attacks
Mauro Conti, Nicola Dragoni, and Viktor Lesyk · 2016
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela · 2020
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Evaluating the susceptibility of pre-trained language models via handcrafted adversarial examples
Hezekiah J. Branch, Jonathan Rodriguez Cefalu, Jeremy McHugh, Leyla Hujer, Aditya Bahl, Daniel del Castillo Iglesias, Ron Heichman, and Ramesh Darwishi · 2022
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Badprompt: Backdoor attacks on continuous prompts, 2022
Xiangrui Cai, Haidong Xu, Sihan Xu, Ying Zhang, and Xiaojie Yuan · 2022
Earlier work this paper cites.
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch · 2022
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Chatgpt: Optimizing language models for dialogue
OpenAI · 2022
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Ignore previous prompt: Attack techniques for language models (2022)
Fábio Perez and Ian Ribeiro · 2022
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Exploring prompt injection attacks
Jose Selvi · 2022
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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
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Prompt injection attacks against GPT-3
Simon Willison · 2022
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React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao · 2022
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Detecting language model attacks with perplexity
Gabriel Alon and Michael Kamfonas · 2023
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Using gpt-eliezer against chatgpt jailbreaking, 2023
R Gorman and Stuart Armstrong · 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
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Securing llm systems against prompt injection, 2023
Rich Harang · 2023
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Baseline defenses for adversarial attacks against aligned language models
Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, and Tom Goldstein · 2023
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Backdoor attacks for in-context learning with language models, 2023
Nikhil Kandpal, Matthew Jagielski, Florian Tramèr, and Nicholas Carlini · 2023
Earlier work this paper cites.
Prompt injection attack against llm-integrated applications
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Zihao Wang, Xiaofeng Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, et al · 2023
Earlier work this paper cites.
Membership inference attacks against language models via neighbourhood comparison
Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, and Taylor Berg-Kirkpatrick · 2023
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OWASP Top 10 for Large Language Model Applications
OWASP · 2023
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Gorilla: Large language model connected with massive apis, 2023
Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez · 2023
Cited alongside, same era.
Toolalpaca: Generalized tool learning for language models with 3000 simulated cases, 2023
Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, and Le Sun · 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
Cited alongside, same era.
Poisoning language models during instruction tuning
Alexander Wan, Eric Wallace, Sheng Shen, and Dan Klein · 2023
Cited alongside, same era.
Decodingtrust: A comprehensive assessment of trustworthiness in gpt models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, et al · 2023
Sleeper agents: Training deceptive llms that persist through safety training
Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M Ziegler, Tim Maxwell, Newton Cheng, et al · 2024
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Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
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The impact of reasoning step length on large language models
Mingyu Jin, Qinkai Yu, Dong Shu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, and Mengnan Du · 2024
Closest in time.
Exploiting programmatic behavior of llms: Dual-use through standard security attacks
Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, and Tatsunori Hashimoto · 2024
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2024
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Cited alongside, same era.
Rab: Provable robustness against backdoor attacks
Maurice Weber, Xiaojun Xu, Bojan Karlaš, Ce Zhang, and Bo Li · 2023
Cited alongside, same era.
Delimiters won’t save you from prompt injection
Simon Willison · 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
Cited alongside, same era.
Poisoning retrieval corpora by injecting adversarial passages
Zexuan Zhong, Ziqing Huang, Alexander Wettig, and Danqi Chen · 2023
Cited alongside, same era.
Conversational health agents: A personalized llm-powered agent framework, 2024
Mahyar Abbasian, Iman Azimi, Amir M. Rahmani, and Ramesh Jain · 2024
Cited alongside, same era.
Model leaderboards, 2024
Artificial Analysis · 2024
Cited alongside, same era.
Claude 3.5 sonnet
Anthropic · 2024
Cited alongside, same era.
Closest in time.
Badedit: Backdooring large language models by model editing
Yanzhou Li, Tianlin Li, Kangjie Chen, Jian Zhang, Shangqing Liu, Wenhan Wang, Tianwei Zhang, and Yang Liu · 2024
Closest in time.
Formalizing and benchmarking prompt injection attacks and defenses
Yupei Liu, Yuqi Jia, Runpeng Geng, Jinyuan Jia, and Neil Zhenqiang Gong · 2024
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A language agent for autonomous driving, 2024
Jiageng Mao, Junjie Ye, Yuxi Qian, Marco Pavone, and Yue Wang · 2024
Closest in time.
Aios: Llm agent operating system, 2024
Kai Mei, Zelong Li, Shuyuan Xu, Ruosong Ye, Yingqiang Ge, and Yongfeng Zhang · 2024
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Chatgpt gpts
OpenAI · 2024
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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, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, dahai li, Zhiyuan Liu, and Maosong Sun · 2024
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Gemma 2: Improving open language models at a practical size
Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al · 2024
Closest in time.
Llama 3.1: An in-depth analysis of the next-generation large language model, 2024
Raja Vavekanand and Kira Sam · 2024
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Slmrec: empowering small language models for sequential recommendation
Wujiang Xu, Qitian Wu, Zujie Liang, Jiaojiao Han, Xuying Ning, Yunxiao Shi, Wenfang Lin, and Yongfeng Zhang · 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
Closest in time.
Navigation as attackers wish? towards building robust embodied agents under federated learning
Yunchao Zhang, Zonglin Di, Kaiwen Zhou, Cihang Xie, and Xin Wang · 2024
Closest in time.
Recommender systems meet large language model agents: A survey
Xi Zhu, Yu Wang, Hang Gao, Wujiang Xu, Chen Wang, Zhiwei Liu, Kun Wang, Mingyu Jin, Linsey Pang, Qingsong Wen, et al · 2024
Closest in time.
Poisonedrag: Knowledge poisoning attacks to retrieval-augmented generation of large language models
Wei Zou, Runpeng Geng, Binghui Wang, and Jinyuan Jia · 2024
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Exploring concept depth: How large language models acquire knowledge and concept at different layers?
Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, and Yongfeng Zhang · 2025
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