English

FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation

Cryptography and Security 2026-04-23 v2 Machine Learning Operating Systems

Abstract

Device-side Large Language Models (LLMs) have witnessed explosive growth, offering higher privacy and availability compared to cloud-side LLMs. During LLM inference, both model weights and user data are valuable, and attackers may even compromise the OS kernel to steal them. ARM TrustZone is the de facto hardware-based isolation technology on mobile devices, used to protect sensitive applications from a compromised OS. However, protecting LLM inference with TrustZone incurs significant overhead due to its inflexible isolation of memory and the NPU. To address these challenges, this paper introduces FlexServe, a fast and secure LLM serving system for mobile devices. It first introduces a Flexible Resource Isolation mechanism to construct Flexible Secure Memory (Flex-Mem) and Flexible Secure NPU (Flex-NPU). Both memory pages and the NPU can be efficiently switched between unprotected and protected modes. Based on these mechanisms, FlexServe designs a fast and secure LLM inference framework within TrustZone's secure world. The LLM-Aware Memory Management and Secure Inference Pipeline are introduced to accelerate inference. A Multi-Model Scheduler is proposed to optimize multi-model workflows. We implement a prototype of FlexServe and compare it with two TrustZone-based strawman designs. The results show that FlexServe achieves an average 10.05×10.05\times speedup in Time to First Token (TTFT) compared to the strawman, and an average 2.44×2.44\times TTFT speedup compared to an optimized strawman with pipeline and secure NPU enabled. For multi-model agent workflows, the end-to-end speedup is up to 24.30×24.30\times and 4.05×4.05\times compared to the strawman and optimized strawman, respectively.

Keywords

Cite

@article{arxiv.2603.09046,
  title  = {FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation},
  author = {Yinpeng Wu and Yitong Chen and Lixiang Wang and Jinyu Gu and Zhichao Hua and Yubin Xia},
  journal= {arXiv preprint arXiv:2603.09046},
  year   = {2026}
}

Comments

13 pages, 11 figures

R2 v1 2026-07-01T11:11:26.576Z