New Framework PPRoute Addresses Privacy Risks in Large Language Model Routing Systems

A new privacy-preserving framework called PPRoute has been developed to address security vulnerabilities in large language model routing systems. These routing systems dynamically select services from different model providers to balance performance and cost, but they introduce an intermediate layer that creates significant privacy risks for user data. The proposed PPRoute framework employs multiple strategies to accelerate encoder inference and nearest neighbor search while maintaining routing quality. It utilizes MPC-friendly operations to boost encoder inference performance. The framework also implements a multiplication-based approach to enhance nearest neighbor search efficiency. These privacy risks in LLM routing had not been systematically studied previously. Although cryptographic techniques like Secure Multi-Party Computation enable privacy-preserving computation, their protocol design and implementation remain under-explored. Naïve implementations of these cryptographic techniques typically incur prohibitive computational overhead that makes them impractical for real-world applications. The research was published on arXiv with identifier 2604.15728v1.