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Modern code completion engines, powered by large language models (LLMs), assist millions of developers with their strong capabilities to generate functionally correct code.
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
Ren, S., Guo, D., Lu, S., Zhou, L., Liu, S., Tang, D., Sundaresan, N., Zhou, M., Blanco, A., and Ma, S · 2009
Earlier work this paper cites.
Explaining static analysis-a perspective
Nachtigall, M., Do, L. N. Q., and Bodden, E · 2019
Earlier work this paper cites.
Evaluating Large Language Models Trained on Code
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
Earlier work this paper cites.
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion
Schuster, R., Song, C., Tromer, E., and Shmatikov, V · 2021
Earlier work this paper cites.
Efficient Training of Language Models to Fill in the Middle
Bavarian, M., Jun, H., Tezak, N., Schulman, J., McLeavey, C., Tworek, J., and Chen, M · 2022
Earlier work this paper cites.
Detecting False Alarms from Automatic Static Analysis Tools: How Far are We?
Kang, H. J., Aw, K. L., and Lo, D · 2022
Earlier work this paper cites.
Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions
Pearce, H., Ahmad, B., Tan, B., Dolan-Gavitt, B., and Karri, R · 2022
Earlier work this paper cites.
Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models
Vaithilingam, P., Zhang, T., and Glassman, E. L · 2022
Earlier work this paper cites.
Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al · 2023
Earlier work this paper cites.
Grounded Copilot: How Programmers Interact with Code-Generating Models
Barke, S., James, M. B., and Polikarpova, N · 2023
Earlier work this paper cites.
MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation
Cassano, F., Gouwar, J., Nguyen, D., Nguyen, S., Phipps-Costin, L., Pinckney, D., Yee, M.-H., Zi, Y., Anderson, C. J., Feldman, M. Q., et al · 2023
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GitHub Copilot X: The AI-powered developer experience, 2023
Dohmke, T · 2023
Earlier work this paper cites.
InCoder: A Generative Model for Code Infilling and Synthesis
Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E., Shi, F., Zhong, R., Yih, S., Zettlemoyer, L., and Lewis, M · 2023
Earlier work this paper cites.
CodeQL - GitHub, 2023
GitHub · 2023
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Large Language Models for Code: Security Hardening and Adversarial Testing
He, J. and Vechev, M · 2023
Earlier work this paper cites.
Baseline defenses for adversarial attacks against aligned language models
Jain, N., Schwarzschild, A., Wen, Y., Somepalli, G., Kirchenbauer, J., Chiang, P.-y., Goldblum, M., Saha, A., Geiping, J., and Goldstein, T · 2023
Earlier work this paper cites.
How Secure is Code Generated by ChatGPT?
Khoury, R., Avila, A. R., Brunelle, J., and Camara, B. M · 2023
Earlier work this paper cites.
Cctest: Testing and repairing code completion systems
Li, Z., Wang, C., Liu, Z., Wang, H., Chen, D., Wang, S., and Gao, C · 2023
Earlier work this paper cites.
Evaluation of Usability Criteria Addressed by Static Analysis Tools on a Large Scale
Nachtigall, M., Schlichtig, M., and Bodden, E · 2023
Cited alongside, same era.
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., and Xiong, C · 2023
Cited alongside, same era.
Do Users Write More Insecure Code with AI Assistants?
Perry, N., Srivastava, M., Kumar, D., and Boneh, D · 2023
Cited alongside, same era.
Code Llama: Open Foundation Models for Code
Rozière, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al · 2023
Cited alongside, same era.
Unhelpful assumptions in software security research
Ryan, I., Roedig, U., and Stol, K.-J · 2023
Cited alongside, same era.
SWE-bench: Can Language Models Resolve Real-world Github Issues?
Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., and Narasimhan, K. R · 2024
Closest in time.
Attribution-guided adversarial code prompt generation for code completion models
Li, X., Meng, G., Liu, S., Xiang, L., Sun, K., Chen, K., Luo, X., and Liu, Y · 2024
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StarCoder 2 and The Stack v2: The Next Generation
Lozhkov, A., Li, R., Allal, L. B., Cassano, F., Lamy-Poirier, J., Tazi, N., Tang, A., Pykhtar, D., Liu, J., Wei, Y., et al · 2024
Closest in time.
Language Model Inversion
Morris, J. X., Zhao, W., Chiu, J. T., Shmatikov, V., and Rush, A. M · 2024
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Introduction - OpenAI API, 2024
OpenAI · 2024
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VSCode Marketplace contains thousands of malicious extensions
Pol, F. H · 2024
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Wilson, D · 2023
Cited alongside, same era.
DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions
Wu, F., Liu, X., and Xiao, C · 2023
Cited alongside, same era.
Low-resource Languages Jailbreak GPT-4
Yong, Z.-X., Menghini, C., and Bach, S. H · 2023
Cited alongside, same era.
Codegeex: A pre-trained model for code generation with multilingual benchmarking on humaneval-x
Zheng, Q., Xia, X., Zou, X., Dong, Y., Wang, S., Xue, Y., Shen, L., Wang, Z., Wang, A., Li, Y., et al · 2023
Cited alongside, same era.
Universal and transferable adversarial attacks on aligned language models
Zou, A., Wang, Z., Carlini, N., Nasr, M., Kolter, J. Z., and Fredrikson, M · 2023
Cited alongside, same era.
TrojanPuzzle: Covertly Poisoning Code-Suggestion Models
Aghakhani, H., Dai, W., Manoel, A., Fernandes, X., Kharkar, A., Kruegel, C., Vigna, G., Evans, D., Zorn, B., and Sim, R · 2024
Cited alongside, same era.
Poisoning Web-Scale Training Datasets is Practical
Carlini, N., Jagielski, M., Choquette-Choo, C. A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., and Tramèr, F · 2024
Cited alongside, same era.
Closest in time.
CodeAttack: Revealing safety generalization challenges of large language models via code completion
Ren, Q., Gao, C., Shao, J., Yan, J., Tan, X., Lam, W., and Ma, L · 2024
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Code Needs Comments: Enhancing Code LLMs with Comment Augmentation, 2024
Song, D., Guo, H., Zhou, Y., Xing, S., Wang, Y., Song, Z., Zhang, W., Guo, Q., Yan, H., Qiu, X., et al · 2024
Closest in time.
Malicious VSCode extensions with millions of installs discovered
Toulas, B · 2024
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Abusing VSCode: From Malicious Extensions to Stolen Credentials (Part 1) Featured Image, 2024
Ward, K. and Kammel, F · 2024
Closest in time.
An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection
Yan, S., Wang, S., Duan, Y., Hong, H., Lee, K., Kim, D., and Hong, Y · 2024
Closest in time.
Jailbreaking leading safety-aligned LLMs with simple adaptive attacks
Andriushchenko, M., Croce, F., and Flammarion, N · 2025
Closest in time.
Steering large language models between code execution and textual reasoning
Chen, Y., Jhamtani, H., Sharma, S., Fan, C., and Wang, C · 2025
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DataCrunch A100 GPU Instances, 2025
DataCrunch · 2025
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Security Weaknesses of Copilot Generated Code in GitHub
Fu, Y., Liang, P., Tahir, A., Li, Z., Shahin, M., and Yu, J · 2025
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Lambda GPU Cloud Pricing, 2025
Lambda Labs · 2025
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Tpia: Towards target-specific prompt injection attack against code-oriented large language models
Yang, Y., Yao, H., Yang, B., He, Y., Li, Y., Zhang, T., and Qin, Z · 2025
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