Utilizing AI-driven code assistants via IDE plugins introduces a specific security risk where the model may suggest code snippets that incorporate…

The statement is true. AI-driven code assistants, which are based on Large Language Models (LLMs), are trained on vast datasets of public code. This training data often includes code that uses deprecated functions, insecure patterns, or outdated libraries. Consequently, the models can suggest these insecure snippets to developers. Furthermore, "hallucination," a known phenomenon in LLMs where the model generates plausible but factually incorrect outputs, can manifest as suggestions for non-existent functions or parameters. If a developer uncritically accepts these suggestions, it can introduce security vulnerabilities or functional bugs into the application.

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1. Pearce, H., et al. (2022). Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions. Proceedings of the 2022 IEEE Symposium on Security and Privacy (SP), 754-771. https://doi.org/10.1109/SP46214.2022.9833571. The study found that approximately 40% of the time, code generated by GitHub Copilot in security-relevant scenarios was vulnerable.

2. O'Leary, D. E. (2023). Googles BARD and Microsofts BING: Generative AI, Hallucinations, and the new AI Arms Race. Intelligent Systems in Accounting, Finance and Management, 30(2), 73-80. https://doi.org/10.1002/isaf.1538. This paper discusses the nature of hallucinations in generative AI models like those used for code assistance.

3. Stanford University. (2023). CS25: Transformers United. Course materials. Lecture notes discuss the limitations of LLMs, including their propensity to generate incorrect or "hallucinated" information based on patterns in their training data rather than factual understanding.