Security Weaknesses of Copilot Generated Code in GitHub

• URL: http://arxiv.org/abs/2310.02059v2
• Date: Thu, 4 Apr 2024 07:53:03 GMT
• Title: Security Weaknesses of Copilot Generated Code in GitHub
• Authors: Yujia Fu, Peng Liang, Amjed Tahir, Zengyang Li, Mojtaba Shahin, Jiaxin Yu, Jinfu Chen,
• Abstract summary: We analyze code snippets generated by GitHub Copilot from GitHub projects. Our analysis identified 452 snippets generated by Copilot, revealing a high likelihood of security issues. It also shows that practitioners should cultivate corresponding security awareness and skills.
• Score: 8.364612094301071
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern code generation tools, utilizing AI models like Large Language Models (LLMs), have gained popularity for producing functional code. However, their usage presents security challenges, often resulting in insecure code merging into the code base. Evaluating the quality of generated code, especially its security, is crucial. While prior research explored various aspects of code generation, the focus on security has been limited, mostly examining code produced in controlled environments rather than real-world scenarios. To address this gap, we conducted an empirical study, analyzing code snippets generated by GitHub Copilot from GitHub projects. Our analysis identified 452 snippets generated by Copilot, revealing a high likelihood of security issues, with 32.8% of Python and 24.5% of JavaScript snippets affected. These issues span 38 different Common Weakness Enumeration (CWE) categories, including significant ones like CWE-330: Use of Insufficiently Random Values, CWE-78: OS Command Injection, and CWE-94: Improper Control of Generation of Code. Notably, eight CWEs are among the 2023 CWE Top-25, highlighting their severity. Our findings confirm that developers should be careful when adding code generated by Copilot and should also run appropriate security checks as they accept the suggested code. It also shows that practitioners should cultivate corresponding security awareness and skills.

Related papers

• Understanding Code Understandability Improvements in Code Reviews [79.16476505761582]
  We analyzed 2,401 code review comments from Java open-source projects on GitHub. 83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted.
  arXiv  Detail & Related papers  (2024-10-29T12:21:23Z)
• HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data [60.75578581719921]
  Large language models (LLMs) have shown great potential for automatic code generation. Recent studies highlight that many LLM-generated code contains serious security vulnerabilities. We introduce HexaCoder, a novel approach to enhance the ability of LLMs to generate secure codes.
  arXiv  Detail & Related papers  (2024-09-10T12:01:43Z)
• Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval [20.959848710829878]
  Large language models (LLMs) have brought significant advancements to code generation and code repair. However, their training using unsanitized data from open-source repositories, like GitHub, raises the risk of inadvertently propagating security vulnerabilities. We aim to present a comprehensive study aimed at precisely evaluating and enhancing the security aspects of code LLMs.
  arXiv  Detail & Related papers  (2024-07-02T16:13:21Z)
• Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answers [4.320393382724067]
  This study empirically compares the vulnerabilities of ChatGPT and StackOverflow snippets. ChatGPT contained 248 vulnerabilities compared to the 302 vulnerabilities found in SO snippets, producing 20% fewer vulnerabilities with a statistically significant difference. Our findings suggest developers are under-educated on insecure code propagation from both platforms.
  arXiv  Detail & Related papers  (2024-03-22T20:06:41Z)
• CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion [117.178835165855]
  This paper introduces CodeAttack, a framework that transforms natural language inputs into code inputs. Our studies reveal a new and universal safety vulnerability of these models against code input. We find that a larger distribution gap between CodeAttack and natural language leads to weaker safety generalization.
  arXiv  Detail & Related papers  (2024-03-12T17:55:38Z)
• Assessing the Security of GitHub Copilot Generated Code -- A Targeted Replication Study [11.644996472213611]
  Recent studies have investigated security issues in AI-powered code generation tools such as GitHub Copilot and Amazon CodeWhisperer. This paper replicates the study of Pearce et al., which investigated security weaknesses in Copilot and uncovered several weaknesses in the code suggested by Copilot. Our results indicate that, even with the improvements in newer versions of Copilot, the percentage of vulnerable code suggestions has reduced from 36.54% to 27.25%.
  arXiv  Detail & Related papers  (2023-11-18T22:12:59Z)
• Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
  We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code. We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
  arXiv  Detail & Related papers  (2023-02-14T18:43:34Z)
• CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models [58.27254444280376]
  Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks. Training data for these models is usually collected from the Internet (e.g., from open-source repositories) and is likely to contain faults and security vulnerabilities. This unsanitized training data can cause the language models to learn these vulnerabilities and propagate them during the code generation procedure.
  arXiv  Detail & Related papers  (2023-02-08T11:54:07Z)
• ReCode: Robustness Evaluation of Code Generation Models [90.10436771217243]
  We propose ReCode, a comprehensive robustness evaluation benchmark for code generation models. We customize over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. With human annotators, we verified that over 90% of the perturbed prompts do not alter the semantic meaning of the original prompt.
  arXiv  Detail & Related papers  (2022-12-20T14:11:31Z)
• Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code? [12.350130201627186]
  We perform a comparative empirical analysis of Copilot-generated code from a security perspective. We investigate whether Copilot is just as likely to introduce the same software vulnerabilities as human developers.
  arXiv  Detail & Related papers  (2022-04-10T18:32:04Z)
• An Empirical Cybersecurity Evaluation of GitHub Copilot's Code Contributions [8.285068188878578]
  GitHub Copilot is a language model trained over open-source GitHub code. Code often contains bugs - and so, it is certain that the language model will have learned from exploitable, buggy code. This raises concerns on the security of Copilot's code contributions.
  arXiv  Detail & Related papers  (2021-08-20T17:30:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.