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The rapid development of large language models (LLMs) has significantly advanced code completion capabilities, giving rise to a new generation of LLM-based Code Completion Tools (LCCTs).
A Practical Handbook of Software Construction
Code Complete. 1993 · 1993
Earlier work this paper cites.
Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21) . 2633–2650
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
Earlier work this paper cites.
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, et al · 2022
Earlier work this paper cites.
Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang. 2022 · 2022
Earlier work this paper cites.
Are large pre-trained language models leaking your personal information?
Jie Huang, Hanyin Shao, and Kevin Chen-Chuan Chang. 2022 · 2022
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Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
Earlier work this paper cites.
Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022 · 2022
Earlier work this paper cites.
Introducing ChatGPT
OpenAI. 2022 · 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 Ray, et al · 2022
Earlier work this paper cites.
Asleep at the keyboard? assessing the security of github copilot’s code contributions. In 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 754–768
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri. 2022 · 2022
Earlier work this paper cites.
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. 2022 · 2022
Earlier work this paper cites.
The (ab) use of open source code to train large language models. In 2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE) . IEEE, 9–10
Ali Al-Kaswan and Maliheh Izadi. 2023 · 2023
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Copyright Conundrums in Generative AI: Github Copilot’s Not-So-Fair Use of Open-Source Licensed Code
Vivek Basanagoudar and Abhijay Srekanth. 2023 · 2023
Earlier work this paper cites.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
Cited alongside, same era.
Jailbreaker: Automated jailbreak across multiple large language model chatbots
Gelei Deng, Yi Liu, Yuekang Li, Kailong Wang, Ying Zhang, Zefeng Li, Haoyu Wang, Tianwei Zhang, and Yang Liu. 2023 · 2023
Cited alongside, same era.
Security weaknesses of copilot generated code in github
Yujia Fu, Peng Liang, Amjed Tahir, Zengyang Li, Mojtaba Shahin, and Jiaxin Yu. 2023 · 2023
Cited alongside, same era.
Llm censorship: A machine learning challenge or a computer security problem?
David Glukhov, Ilia Shumailov, Yarin Gal, Nicolas Papernot, and Vardan Papyan. 2023 · 2023
Cited alongside, same era.
Masterkey: Automated jailbreaking of large language model chatbots. In Proc. ISOC NDSS
Gelei Deng, Yi Liu, Yuekang Li, Kailong Wang, Ying Zhang, Zefeng Li, Haoyu Wang, Tianwei Zhang, and Yang Liu. 2024 · 2024
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Google Perspective API
Google. 2024 · 2024
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Your Code Secret Belongs to Me: Neural Code Completion Tools Can Memorize Hard-Coded Credentials
Yizhan Huang, Yichen Li, Weibin Wu, Jianping Zhang, and Michael R Lyu. 2024 · 2024
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Exploiting programmatic behavior of llms: Dual-use through standard security attacks. In 2024 IEEE Security and Privacy Workshops (SPW) . IEEE, 132–143
Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, and Tatsunori Hashimoto. 2024 · 2024
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Assessing the Security of GitHub Copilot’s Generated Code-A Targeted Replication Study. In 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 435–444
Vahid Majdinasab, Michael Joshua Bishop, Shawn Rasheed, Arghavan Moradidakhel, Amjed Tahir, and Foutse Khomh. 2024 · 2024
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Pretraining language models with human preferences. In International Conference on Machine Learning . PMLR, 17506–17533
Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher Buckley, Jason Phang, Samuel R Bowman, and Ethan Perez. 2023 · 2023
Cited alongside, same era.
Jailbreaking chatgpt via prompt engineering: An empirical study
Yi Liu, Gelei Deng, Zhengzi Xu, Yuekang Li, Yaowen Zheng, Ying Zhang, Lida Zhao, Tianwei Zhang, Kailong Wang, and Yang Liu. 2023 · 2023
Cited alongside, same era.
Analyzing leakage of personally identifiable information in language models. In 2023 IEEE Symposium on Security and Privacy (SP) . IEEE, 346–363
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, and Santiago Zanella-Béguelin. 2023 · 2023
Cited alongside, same era.
Inverse scaling: When bigger isn’t better
Ian R McKenzie, Alexander Lyzhov, Michael Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Aaron Kirtland, Alexis Ross, Alisa Liu, et al · 2023
Cited alongside, same era.
Fine-tuning aligned language models compromises safety, even when users do not intend to!
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. 2023 · 2023
Cited alongside, same era.
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. 2023 · 2023
Cited alongside, same era.
Shadow alignment: The ease of subverting safely-aligned language models
Xianjun Yang, Xiao Wang, Qi Zhang, Linda Petzold, William Yang Wang, Xun Zhao, and Dahua Lin. 2023 · 2023
Cited alongside, same era.
Demystifying Practices, Challenges and Expected Features of Using GitHub Copilot
Beiqi Zhang, Peng Liang, Xiyu Zhou, Aakash Ahmad, and Muhammad Waseem. 2023 · 2023
Cited alongside, same era.
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CWE-200: Exposure of Sensitive Information to an Unauthorized Actor
MITRE. 2024 · 2024
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Usage policies
OpenAI. 2024 · 2024
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AI writes, we analyze: The ChatGPT python code saga. In 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR) . IEEE, 177–181
Md Fazle Rabbi, Arifa Champa, Minhaz Zibran, and Md Rakibul Islam. 2024 · 2024
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Exploring safety generalization challenges of large language models via code
Qibing Ren, Chang Gao, Jing Shao, Junchi Yan, Xin Tan, Wai Lam, and Lizhuang Ma. 2024 · 2024
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Bugs in large language models generated code
Florian Tambon, Arghavan Moradi Dakhel, Amin Nikanjam, Foutse Khomh, Michel C Desmarais, and Giuliano Antoniol. 2024 · 2024
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Jailbroken: How does llm safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2024 · 2024
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GitHub copilot drives revenue growth amid subscriber base expansion
Lindsey Wilkinson. 2024 · 2024
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