一句话：提出将安全内嵌于模型训练与评估的框架，防止LLM生成含漏洞代码。
论文关注大语言模型（LLM）进入软件开发流程后生成易受 SQL 注入、跨站脚本和缓冲区溢出等攻击的代码，其根源是训练数据中的不安全模式。现有静态/动态分析与提示工程等缓解措施多为事后反应，而非预防。作者探讨微调、对抗训练等模型训练方法，并提出一个评估框架，将安全目标直接嵌入 AI 辅助编程的训练与评估环节，旨在建立安全 AI 驱动编程的稳健标准。摘要未报告具体实验结果，贡献主要是方法论与框架倡议。
The integration of Large Language Models (LLMs) into software development workflows has transformed automated programming but introduced significant security challenges. LLMs often generate vulnerable code due to the insecure patterns present in training data, leading to the generation of code vulnerable to threats such as SQL injection, cross-site scripting, and buffer overflows. Existing mitigation strategies, including static and dynamic analysis tools and prompt engineering, are reactive rather than preventive. Recent advances in model training, such as fine-tuning and adversarial training, offer promising avenues for enhancing the security of LLM-generated code. This paper explores different methodologies and proposes an evaluation framework to embed security directly into AI-assisted programming. By integrating security into model training and assessment, we aim to establish a robust standard for secure AI-driven programming.