Mini-applications, commonly referred to as mini-apps, are compact software programs embedded within larger applications or platforms, offering targeted functionality without the need for separate installations. Typically web-based or cloud-hosted, these mini-apps streamline user experiences by providing focused services accessible through web browsers or mobile apps. Their simplicity, speed, and integration capabilities make them valuable additions to messaging platforms, social media networks, e-commerce sites, and various digital environments. WeChat Mini Programs, a prominent feature of China's leading messaging app, exemplify this trend, offering users a seamless array of services without additional downloads. Leveraging WeChat's extensive user base and payment infrastructure, Mini Programs facilitate efficient transactions and bridge online and offline experiences, shaping China's digital landscape significantly. This paper investigates the potential of employing Large Language Models (LLMs) to detect privacy breaches within WeChat Mini Programs. Given the widespread use of Mini Programs and growing concerns about data privacy, this research seeks to determine if LLMs can effectively identify instances of privacy leakage within this ecosystem. Through meticulous analysis and experimentation, we aim to highlight the efficacy of LLMs in safeguarding user privacy and security within the WeChat Mini Program environment, thereby contributing to a more secure digital landscape.

翻译：小程序，通常被称为迷你应用程序，是嵌入在大型应用程序或平台中的紧凑型软件程序，无需单独安装即可提供针对性功能。这些小程序通常基于网络或云端托管，通过网页浏览器或移动应用提供聚焦的服务，从而简化用户体验。其简洁性、快速性和集成能力使其成为消息平台、社交媒体网络、电商网站及各类数字环境中的重要补充。微信小程序作为中国领先即时通讯应用的核心功能，体现了这一趋势，为用户提供无需额外下载即可获取的无缝服务。借助微信庞大的用户基础与支付基础设施，小程序实现了高效交易，融合线上与线下体验，深刻塑造了中国数字生态。本文探究了利用大型语言模型（LLMs）检测微信小程序中隐私泄露的潜力。鉴于小程序的广泛使用以及日益增长的数据隐私担忧，本研究旨在确定LLMs是否能有效识别该生态中的隐私泄露实例。通过细致的分析与实验，我们力图揭示LLMs在微信小程序环境中保障用户隐私与安全的效能，从而为构建更安全的数字环境做出贡献。