In recent years, with the continuous expansion of code scale and the increasing complexity of functional requirements and system architectures in the field of software development, automated tools have become a core support for improving development efficiency. Among these tools, generative large language models (LLMs) have been widely applied in software development. Although LLMs can significantly shorten the development cycle, studies have shown that the code they generate often contains security vulnerabilities. These vulnerabilities may pose risks to the system.Existing research has found that the outputs of LLMs are influenced by differences in prompt languages. However, research and evaluation on the security of code generated using different prompt languages remain insufficient. This study aims to explore the impact of English and Chinese input prompt languages on code security and to conduct a systematic evaluation of this impact. Specifically, English and Chinese texts are used to instruct code generation in LLMs, and the differences in the security of the generated code are analyzed.The experimental results indicate that when English is used as the prompt language, the number of vulnerabilities and bugs in the code generated by LLMs is relatively smaller. In contrast, when Chinese is used as the prompt language, the generated code is more prone to security vulnerabilities.