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.

本文解决了大型语言模型（LLMs）生成的代码在安全性上的不足，尤其是在现实应用中面临的风险。通过对不同模型生成的代码进行安全合规性评估，发现关键的认证机制、会话管理和输入验证等方面存在严重漏洞，强调了人类专家在确保安全软件部署中的必要性，并呼吁建立更强大的安全评估框架。

大型语言模型生成的网络应用代码的隐患：针对代码生成能力的安全评估