| Sumario: | 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.
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