A systematic literature review on the impact of AI models on the security of code generation

• Published in: Frontiers in big data Vol. 7; p. 1386720
• Main Authors: Negri-Ribalta, Claudia, Geraud-Stewart, Rémi, Sergeeva, Anastasia, Lenzini, Gabriele
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 13.05.2024
• Subjects: artificial intelligence; Big Data; code generation; programming; security; software engineering; security; software engineering; code generation; artificial intelligence; programming
• ISSN: 2624-909X; 2624-909X
• DOI: 10.3389/fdata.2024.1386720

Artificial Intelligence (AI) is increasingly used as a helper to develop computing programs. While it can boost software development and improve coding proficiency, this practice offers no guarantee of security. On the contrary, recent research shows that some AI models produce software with vulnerabilities. This situation leads to the question: How serious and widespread are the security flaws in code generated using AI models? Through a systematic literature review, this work reviews the state of the art on how AI models impact software security. It systematizes the knowledge about the risks of using AI in coding security-critical software. It reviews what security flaws of well-known vulnerabilities (e.g., the MITRE CWE Top 25 Most Dangerous Software Weaknesses) are commonly hidden in AI-generated code. It also reviews works that discuss how vulnerabilities in AI-generated code can be exploited to compromise security and lists the attempts to improve the security of such AI-generated code. Overall, this work provides a comprehensive and systematic overview of the impact of AI in secure coding. This topic has sparked interest and concern within the software security engineering community. It highlights the importance of setting up security measures and processes, such as code verification, and that such practices could be customized for AI-aided code production.