Web-based Large Language Model (LLM) services have been widely adopted and have become an integral part of our Internet experience. Third-party plugins enhance the functionalities of LLM by enabling access to real-world data and services. However, the privacy consequences associated with these services and their third-party plugins are not well understood. Sensitive prompt data are stored, processed, and shared by cloud-based LLM providers and third-party plugins. In this paper, we propose Casper, a prompt sanitization technique that aims to protect user privacy by detecting and removing sensitive information from user inputs before sending them to LLM services. Casper runs entirely on the user's device as a browser extension and does not require any changes to the online LLM services. At the core of Casper is a three-layered sanitization mechanism consisting of a rule-based filter, a Machine Learning (ML)-based named entity recognizer, and a browser-based local LLM topic identifier. We evaluate Casper on a dataset of 4000 synthesized prompts and show that it can effectively filter out Personal Identifiable Information (PII) and privacy-sensitive topics with high accuracy, at 98.5% and 89.9%, respectively.

本研究解决了基于网络的大型语言模型服务中第三方插件对用户隐私的潜在威胁，特别是敏感提示数据的存储和共享问题。提出的Casper技术通过在用户设备上运行的浏览器扩展，采用三层清洁机制，检测并移除用户输入中的敏感信息。实验结果表明，Casper在过滤个人可识别信息和隐私敏感主题方面具有高达98.5%和89.9%的准确率，显著增强了用户隐私保护。

Casper：用于保护用户隐私的网络大型语言模型提示清洁技术