Large language models (LLMs) have brought significant advancements to code generation, benefiting both novice and experienced developers. However, their training using unsanitized data from open-source repositories, like GitHub, introduces the risk of inadvertently propagating security vulnerabilities. To effectively mitigate this concern, this paper presents a comprehensive study focused on evaluating and enhancing code LLMs from a software security perspective. We introduce SecuCoGen\footnote{SecuCoGen has been uploaded as supplemental material and will be made publicly available after publication.}, a meticulously curated dataset targeting 21 critical vulnerability types. SecuCoGen comprises 180 samples and serves as the foundation for conducting experiments on three crucial code-related tasks: code generation, code repair and vulnerability classification, with a strong emphasis on security. Our experimental results reveal that existing models often overlook security concerns during code generation, leading to the generation of vulnerable code. To address this, we propose effective approaches to mitigate the security vulnerabilities and enhance the overall robustness of code generated by LLMs. Moreover, our study identifies weaknesses in existing models' ability to repair vulnerable code, even when provided with vulnerability information. Additionally, certain vulnerability types pose challenges for the models, hindering their performance in vulnerability classification. Based on these findings, we believe our study will have a positive impact on the software engineering community, inspiring the development of improved methods for training and utilizing LLMs, thereby leading to safer and more trustworthy model deployment.

大型语言模型（LLMs）在代码生成方面取得了显著进展，但它们的训练使用了来自开源代码库（如GitHub）的未经筛选的数据，存在意外传播安全漏洞的风险。为了有效地减轻这一问题，本文从软件安全的角度对代码LLMs进行了全面研究，并提出了SecuCoGen数据集，用于评估和增强代码LLMs的安全性能。研究结果发现，现有模型在代码生成中经常忽视安全问题，提出了有效的方法来解决安全漏洞，并提高代码的整体稳健性。此外，研究还发现现有模型在修复漏洞代码方面存在问题，并且某些漏洞类型对模型构成挑战。基于这些发现，我们相信本研究将对软件工程社区产生积极影响，激发改进LLMs训练和使用方法的开发，从而实现更安全、更可信的模型部署。

加强大型语言模型进行安全代码生成：基于数据集的漏洞缓解研究