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

arXiv: https://arxiv.org/abs/2504.20612 · HTML: https://mlsanity.com/paper/2504.20612/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [How Secure is Code Generated by ChatGPT?](https://mlsanity.com/paper/2304.09655/) (2023)
- [LLMs in Web Development: Evaluating LLM-Generated PHP Code Unveiling Vulnerabilities and Limitations](https://mlsanity.com/paper/2404.14459/) (2024)

## Built on by (most cited first, 7 of 7)

- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis](https://mlsanity.com/paper/2508.14727/) (2025)
- [EVALOOOP: A Self-Consistency-Centered Framework for Assessing Large Language Model Robustness in Programming](https://mlsanity.com/paper/2505.12185/) (2025)
- [Position: Natural Language Should Not Fully Replace Formal Languages](https://mlsanity.com/paper/2607.20432/) (2026)
- [SoK: Understanding (New) Security Issues Across AI4Code Use Cases](https://mlsanity.com/paper/2512.18456/) (2025)
- [Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety](https://mlsanity.com/paper/2604.12311/) (2026)
- [The Illusion of Secure LLM Code: Closing the Security Gap via Iterative Reprompting](https://mlsanity.com/paper/2607.23710/) (2026)

## Abstract (arXiv)

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.