Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866915140462444544 |
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| author | Fu, Yujia Liang, Peng Tahir, Amjed Li, Zengyang Shahin, Mojtaba Yu, Jiaxin Chen, Jinfu |
| author_facet | Fu, Yujia Liang, Peng Tahir, Amjed Li, Zengyang Shahin, Mojtaba Yu, Jiaxin Chen, Jinfu |
| contents | Modern code generation tools utilizing AI models like Large Language Models (LLMs) have gained increased popularity due to their ability to produce functional code. However, their usage presents security challenges, often resulting in insecure code merging into the code base. Thus, 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 open source development scenarios. To address this gap, we conducted an empirical study, analyzing code snippets generated by GitHub Copilot and two other AI code generation tools (i.e., CodeWhisperer and Codeium) from GitHub projects. Our analysis identified 733 snippets, revealing a high likelihood of security weaknesses, with 29.5% of Python and 24.2% of JavaScript snippets affected. These issues span 43 Common Weakness Enumeration (CWE) categories, including significant ones like CWE-330: Use of Insufficiently Random Values, CWE-94: Improper Control of Generation of Code, and CWE-79: Cross-site Scripting. Notably, eight of those CWEs are among the 2023 CWE Top-25, highlighting their severity. We further examined using Copilot Chat to fix security issues in Copilot-generated code by providing Copilot Chat with warning messages from the static analysis tools, and up to 55.5% of the security issues can be fixed. We finally provide the suggestions for mitigating security issues in generated code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_02059 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study Fu, Yujia Liang, Peng Tahir, Amjed Li, Zengyang Shahin, Mojtaba Yu, Jiaxin Chen, Jinfu Software Engineering Cryptography and Security Modern code generation tools utilizing AI models like Large Language Models (LLMs) have gained increased popularity due to their ability to produce functional code. However, their usage presents security challenges, often resulting in insecure code merging into the code base. Thus, 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 open source development scenarios. To address this gap, we conducted an empirical study, analyzing code snippets generated by GitHub Copilot and two other AI code generation tools (i.e., CodeWhisperer and Codeium) from GitHub projects. Our analysis identified 733 snippets, revealing a high likelihood of security weaknesses, with 29.5% of Python and 24.2% of JavaScript snippets affected. These issues span 43 Common Weakness Enumeration (CWE) categories, including significant ones like CWE-330: Use of Insufficiently Random Values, CWE-94: Improper Control of Generation of Code, and CWE-79: Cross-site Scripting. Notably, eight of those CWEs are among the 2023 CWE Top-25, highlighting their severity. We further examined using Copilot Chat to fix security issues in Copilot-generated code by providing Copilot Chat with warning messages from the static analysis tools, and up to 55.5% of the security issues can be fixed. We finally provide the suggestions for mitigating security issues in generated code. |
| title | Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study |
| topic | Software Engineering Cryptography and Security |
| url | https://arxiv.org/abs/2310.02059 |