The Hidden Risks of LLM-Generated Web Application Code: A Security-Centric Evaluation of Code Generation Capabilities in Large Language Models

• URL: http://arxiv.org/abs/2504.20612v1
• Date: Tue, 29 Apr 2025 10:23:11 GMT
• Title: The Hidden Risks of LLM-Generated Web Application Code: A Security-Centric Evaluation of Code Generation Capabilities in Large Language Models
• Authors: Swaroop Dora, Deven Lunkad, Naziya Aslam, S. Venkatesan, Sandeep Kumar Shukla,
• Abstract summary: This paper uses predefined security parameters to evaluate the security compliance of LLM-generated code across multiple models.<n>The analysis reveals critical vulnerabilities in authentication mechanisms, session management, input validation and HTTP security headers.<n>Our findings underscore that human expertise is crucial to ensure secure software deployment or review of LLM-generated code.
• Score: 0.769672852567215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of Large Language Models (LLMs) has enhanced software development processes, minimizing the time and effort required for coding and enhancing developer productivity. However, despite their potential benefits, code generated by LLMs has been shown to generate insecure code in controlled environments, raising critical concerns about their reliability and security in real-world applications. This paper uses predefined security parameters to evaluate the security compliance of LLM-generated code across multiple models, such as ChatGPT, DeepSeek, Claude, Gemini and Grok. The analysis reveals critical vulnerabilities in authentication mechanisms, session management, input validation and HTTP security headers. Although some models implement security measures to a limited extent, none fully align with industry best practices, highlighting the associated risks in automated software development. Our findings underscore that human expertise is crucial to ensure secure software deployment or review of LLM-generated code. Also, there is a need for robust security assessment frameworks to enhance the reliability of LLM-generated code in real-world applications.

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