Unsafe Code Still a Hurdle Copilot Must Clear.
This paper investigates the security implications of code generated by large language models (LLMs) like GitHub Copilot, which is trained on public repositories to assist developers with code completion. A systematic analysis of 89 programming scenarios revealed that around 40% of Copilot's outputs...
| Publicado en: | Communications of the ACM Vol. 68; no. 2; pp. 95 - 96 |
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| Formato: | Artículo |
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Association for Computing Machinery
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=182365556&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 182365556 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Feb2025 vid: 68 iid: 2 pid: 68 pub: Association for Computing Machinery artinfo: ui: 182365556 10.1145/3660529 ppf: 95 ppct: 1 formats: tig: atl: Unsafe Code Still a Hurdle Copilot Must Clear. aug: au: Holz, Thorsten affil: CISPA Helmholtz Center for Information Security, Saarbrücken, Germany su: Language models Computer software development Generative artificial intelligence Computer programming Computer software quality control sug: subj: Language models Computer software development Generative artificial intelligence Computer programming Computer software quality control ab: This paper investigates the security implications of code generated by large language models (LLMs) like GitHub Copilot, which is trained on public repositories to assist developers with code completion. A systematic analysis of 89 programming scenarios revealed that around 40% of Copilot's outputs contained vulnerabilities, often reflecting insecure coding practices present in its training data. The study highlights the need for prompt engineering, manual oversight, and secure coding standards to mitigate risks, emphasizing the importance of combining LLMs with traditional security practices for reliable and safe software development. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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