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Retrieval augmented generation (RAG) is a process where a large language model (LLM) retrieves useful information from a database and then generates the responses.
The confused deputy: (or why capabilities might have been invented)
Norm Hardy · 1988
Earlier work this paper cites.
A secure identity-based capability system
Li Gong et al · 1989
Earlier work this paper cites.
Capability-based protection in the mungi operating system
Jerry Vochteloo, Stephen Russell, and Gernot Heiser · 1993
Earlier work this paper cites.
Jflow: Practical mostly-static information flow control
Andrew C Myers · 1999
Earlier work this paper cites.
Eros: a fast capability system
Jonathan S Shapiro, Jonathan M Smith, and David J Farber · 1999
Earlier work this paper cites.
{ \{ POSIX } \} access control lists on linux
Andreas Grünbacher · 2003
Earlier work this paper cites.
Labels and event processes in the asbestos operating system
Petros Efstathopoulos, Maxwell Krohn, Steve VanDeBogart, Cliff Frey, David Ziegler, Eddie Kohler, David Mazieres, Frans Kaashoek, and Robert Morris · 2005
Earlier work this paper cites.
Information Flow Control for Secure Web Sites
Maxwell Norman Krohn · 2008
Earlier work this paper cites.
Capsicum: Practical capabilities for { \{ UNIX } \}
Robert NM Watson, Jonathan Anderson, Ben Laurie, and Kris Kennaway · 2010
Earlier work this paper cites.
Making information flow explicit in histar
Nickolai Zeldovich, Silas Boyd-Wickizer, Eddie Kohler, and David Mazieres · 2011
Earlier work this paper cites.
Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
Earlier work this paper cites.
Do not blame users for misconfigurations
Tianyin Xu, Jiaqi Zhang, Peng Huang, Jing Zheng, Tianwei Sheng, Ding Yuan, Yuanyuan Zhou, and Shankar Pasupathy · 2013
Earlier work this paper cites.
Practical { \{ DIFC } \} enforcement on android
Adwait Nadkarni, Benjamin Andow, William Enck, and Somesh Jha · 2016
Earlier work this paper cites.
On access control, capabilities, their equivalence, and confused deputy attacks
Vineet Rajani, Deepak Garg, and Tamara Rezk · 2016
Earlier work this paper cites.
Stealing machine learning models via prediction { \{ APIs } \}
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
Earlier work this paper cites.
Early detection of configuration errors to reduce failure damage
Tianyin Xu, Xinxin Jin, Peng Huang, Yuanyuan Zhou, Shan Lu, Long Jin, and Shankar Pasupathy · 2016
Earlier work this paper cites.
Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
Earlier work this paper cites.
HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning · 2018
Earlier work this paper cites.
Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave · 2020
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
Earlier work this paper cites.
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, et al · 2020
Earlier work this paper cites.
Approximate nearest neighbor negative contrastive learning for dense text retrieval
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk · 2020
Cited alongside, same era.
Poisoning the unlabeled dataset of { \{ Semi-Supervised } \} learning
Nicholas Carlini · 2021
Cited alongside, same era.
Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
Cited alongside, same era.
Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave · 2021
Cited alongside, same era.
Cloud misconfigurations: The hidden but preventable threat to cloud data, 2021
Assaf Morag · 2021
Smoothllm: Defending large language models against jailbreaking attacks
Alexander Robey, Eric Wong, Hamed Hassani, and George J Pappas · 2023
Later among the works it cites.
Improving the domain adaptation of retrieval augmented generation (rag) models for open domain question answering
Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, and Suranga Nanayakkara · 2023
Later among the works it cites.
Information flow control in machine learning through modular model architecture
Trishita Tiwari, Suchin Gururangan, Chuan Guo, Weizhe Hua, Sanjay Kariyappa, Udit Gupta, Wenjie Xiong, Kiwan Maeng, Hsien-Hsin S Lee, and G Edward Suh · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
Research reveals that iam is too often permissive and misconfigured, 2021
Cedric Pernetf · 2021
Cited alongside, same era.
Unit 42 cloud threat report update: Cloud security weakens as more organizations fail to secure iam, 2021
Nathaniel Quist · 2021
Cited alongside, same era.
Static detection of silent misconfigurations with deep interaction analysis
Jialu Zhang, Ruzica Piskac, Ennan Zhai, and Tianyin Xu · 2021
Cited alongside, same era.
Iseeq: Information seeking question generation using dynamic meta-information retrieval and knowledge graphs
Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, and Hongxia Jin · 2022
Cited alongside, same era.
Poisoning attacks against machine learning: Can machine learning be trustworthy?
Alina Oprea, Anoop Singhal, and Apostol Vassilev · 2022
Cited alongside, same era.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba · 2022
Cited alongside, same era.
https://www.microsoft.com/en-us/microsoft-365/blog/2023/05/01/microsoft-365-innovatio[…]d-collaboration-tools-help-small-and-medium-businesses-grow/
2023
Cited alongside, same era.
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Later among the works it cites.
A prompt pattern catalog to enhance prompt engineering with chatgpt
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C Schmidt · 2023
Later among the works it cites.
Rethinking privacy in machine learning pipelines from an information flow control perspective
Lukas Wutschitz, Boris Köpf, Andrew Paverd, Saravan Rajmohan, Ahmed Salem, Shruti Tople, Santiago Zanella-Béguelin, Menglin Xia, and Victor Rühle · 2023
Later among the works it cites.
Iag: Induction-augmented generation framework for answering reasoning questions
Zhebin Zhang, Xinyu Zhang, Yuanhang Ren, Saijiang Shi, Meng Han, Yongkang Wu, Ruofei Lai, and Zhao Cao · 2023
Later among the works it cites.
https://www.theverge.com/2024/3/1/24088026/copilot-for-onedrive-file-find-summary-prompts-natural-language
2024
Closest in time.
https://hackaday.com/2024/02/28/air-canadas-chatbot-why-rag-is-better-than-an-llm-for-facts/
2024
Closest in time.
https://www.microsoft.com/en-us/microsoft-365/blog/2024/01/15/expanding-copilot-for-microsoft-365-to-businesses-of-all-sizes/
2024
Closest in time.
Seven failure points when engineering a retrieval augmented generation system
Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu, Zach Brannelly, and Mohamed Abdelrazek · 2024
Closest in time.
Stealing part of a production language model
Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, et al · 2024
Closest in time.
Trojanrag: Retrieval-augmented generation can be backdoor driver in large language models
Pengzhou Cheng, Yidong Ding, Tianjie Ju, Zongru Wu, Wei Du, Ping Yi, Zhuosheng Zhang, and Gongshen Liu · 2024
Closest in time.
Pandora: Jailbreak gpts by retrieval augmented generation poisoning
Gelei Deng, Yi Liu, Kailong Wang, Yuekang Li, Tianwei Zhang, and Yang Liu · 2024
Closest in time.
Whispers in the machine: Confidentiality in llm-integrated systems
Jonathan Evertz, Merlin Chlosta, Lea Schönherr, and Thorsten Eisenhofer · 2024
Closest in time.
G-retriever: Retrieval-augmented generation for textual graph understanding and question answering
Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi · 2024
Closest in time.
A new era in llm security: Exploring security concerns in real-world llm-based systems
Fangzhou Wu, Ning Zhang, Somesh Jha, Patrick McDaniel, and Chaowei Xiao · 2024
Closest in time.
Autoattacker: A large language model guided system to implement automatic cyber-attacks
Jiacen Xu, Jack W Stokes, Geoff McDonald, Xuesong Bai, David Marshall, Siyue Wang, Adith Swaminathan, and Zhou Li · 2024
Closest in time.
Trojllm: A black-box trojan prompt attack on large language models
Jiaqi Xue, Mengxin Zheng, Ting Hua, Yilin Shen, Yepeng Liu, Ladislau Bölöni, and Qian Lou · 2024
Closest in time.
The good and the bad: Exploring privacy issues in retrieval-augmented generation (rag)
Shenglai Zeng, Jiankun Zhang, Pengfei He, Yue Xing, Yiding Liu, Han Xu, Jie Ren, Shuaiqiang Wang, Dawei Yin, Yi Chang, 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
Closest in time.