LoRATEE: A Secure and Efficient Inference Framework for Multi-Tenant LoRA LLMs Based on TEE

Abstract

Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning approach that adaptes pre-trained Large Language Models (LLMs) to multi-tenant tasks by generating a variety of LoRA adapters. However, this approach faces significant security challenges and is particularly susceptible to malicious servers stealing model parameters and sensitive data. Existing research on addressing security risks in multi-tenant environments remains constrained and insufficient. This paper explores the security challenges and proposes the LoRATEE framework, which embeds LoRA adapters within a server-side Trusted Execution Environment (TEE) and employs a lightweight One-Time Pad (OTP) encryption mechanism to ensure secure data transmission. Additionally, we design a dynamic LoRA adapter prefetching mechanism to reduce I/O latency. Moreover, a LoRA adapter module equivalence-sharing strategy based on Parameter-Efficient Fine-Tuning (PEFT) and minimalist design principles was introduced to optimize adapters loading. Experimental results show that LoRATEE maintains inference efficiency while securing multi-tenant LoRA LLMs systems.