SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code

• URL: http://arxiv.org/abs/2506.05692v3
• Date: Fri, 20 Jun 2025 12:42:57 GMT
• Title: SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code
• Authors: Xinghang Li, Jingzhe Ding, Chao Peng, Bing Zhao, Xiang Gao, Hongwan Gao, Xinchen Gu,
• Abstract summary: We introduce SafeGenBench, a benchmark specifically designed to assess the security of LLM-generated code.<n>The dataset encompasses a wide range of common software development scenarios and vulnerability types.<n>Through the empirical evaluation of state-of-the-art LLMs on SafeGenBench, we reveal notable deficiencies in their ability to produce vulnerability-free code.
• Score: 7.209766132478914
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The code generation capabilities of large language models(LLMs) have emerged as a critical dimension in evaluating their overall performance. However, prior research has largely overlooked the security risks inherent in the generated code. In this work, we introduce SafeGenBench, a benchmark specifically designed to assess the security of LLM-generated code. The dataset encompasses a wide range of common software development scenarios and vulnerability types. Building upon this benchmark, we develop an automatic evaluation framework that leverages both static application security testing(SAST) and LLM-based judging to assess the presence of security vulnerabilities in model-generated code. Through the empirical evaluation of state-of-the-art LLMs on SafeGenBench, we reveal notable deficiencies in their ability to produce vulnerability-free code. Our findings highlight pressing challenges and offer actionable insights for future advancements in the secure code generation performance of LLMs. The data and code will be released soon.

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