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.
@article{arxiv.2506.05692,
title = {SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code},
author = {Xinghang Li and Jingzhe Ding and Chao Peng and Bing Zhao and Xiang Gao and Hongwan Gao and Xinchen Gu},
journal= {arXiv preprint arXiv:2506.05692},
year = {2025}
}