StrongBox: A GPU TEE on Arm Endpoints.

Yunjie Deng, Chenxu Wang, Shunchang Yu, Shiqing Liu, Zhenyu Ning, Kevin Leach, Jin Li, Shoumeng Yan, Zhengyu He, Jiannong Cao, Fengwei Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

A wide range of Arm endpoints leverage integrated and discrete GPUs to accelerate computation such as image processing and numerical processing applications. However, in spite of these important use cases, Arm GPU security has yet to be scrutinized by the community. By exploiting vulnerabilities in the kernel, attackers can directly access sensitive data used during GPU computing, such as personally-identifiable image data in computer vision tasks. Existing work has used Trusted Execution Environments (TEEs) to address GPU security concerns on Intel-based platforms, while there are numerous architectural differences that lead to novel technical challenges in deploying TEEs for Arm GPUs. In addition, extant Arm-based GPU defenses are intended for secure machine learning, and lack generality. There is a need for generalizable and efficient Arm-based GPU security mechanisms.

To address these problems, we present StrongBox, the first GPU TEE for secured general computation on Arm endpoints. During confidential computation on Arm GPUs, StrongBox provides an isolated execution environment by ensuring exclusive access to the GPU. Our approach is based in part on a dynamic, fine-grained memory protection policy as Arm-based GPUs typically share a unified memory with the CPU, a stark contrast with Intel-based platforms. Furthermore, by characterizing GPU buffers as secure and non-secure, StrongBox reduces redundant security introspection operations to control access to sensitive data used by the GPU, ultimately reducing runtime overhead. Our design leverages the widely-deployed Arm TrustZone and generic Arm features, without hardware modification or architectural changes. We prototype StrongBox using an off-the-shelf Arm Mali GPU and perform an extensive evaluation. Our results show that StrongBox successfully ensures the GPU computing security with a low (4.70% - 15.26%) overhead across several indicative benchmarks.
Original languageEnglish
Title of host publicationCCS '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
Pages769–783
Number of pages15
DOIs
Publication statusPublished - Nov 2022
Event2022 ACM SIGSAC Conference on Computer and Communications Security - Los Angeles, United States
Duration: 7 Nov 202211 Nov 2022
https://www.sigsac.org/ccs/CCS2022/

Conference

Conference2022 ACM SIGSAC Conference on Computer and Communications Security
Abbreviated titleACM CCS 2022
Country/TerritoryUnited States
CityLos Angeles
Period7/11/2211/11/22
Internet address

Keywords

  • Arm endpoint GPU;
  • Trusted execution

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