StrongBox: A GPU TEE on Arm Endpoints

Yunjie Deng, Chenxu Wang, Shunchang Yu, Shiqing Liu, Zhenyu Ning, Kevin Leach, Jin Li, Shoumeng Yan, Zheng-hao He, Jiannong Cao, Fengwei Zhang
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引用次数: 11

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.
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StrongBox: Arm端点上的GPU TEE
广泛的Arm端点利用集成和离散gpu来加速计算,如图像处理和数值处理应用程序。然而,尽管有这些重要的用例,Arm GPU的安全性还没有被社区仔细审查。通过利用内核漏洞,攻击者可以直接访问GPU计算过程中使用的敏感数据,例如计算机视觉任务中的个人身份图像数据。现有的工作已经使用可信执行环境(tee)来解决基于英特尔平台上的GPU安全问题,而在为Arm GPU部署tee时,存在许多架构差异,导致新的技术挑战。此外,现有的基于arm的GPU防御旨在安全的机器学习,缺乏通用性。有一个通用的和有效的基于arm的GPU安全机制的需求。为了解决这些问题,我们提出了StrongBox,这是第一个用于Arm端点上安全通用计算的GPU TEE。在Arm GPU上进行机密计算时,StrongBox通过确保对GPU的独占访问提供了一个隔离的执行环境。我们的方法部分基于动态的、细粒度的内存保护策略,因为基于arm的gpu通常与CPU共享统一的内存,这与基于intel的平台形成鲜明对比。此外,通过将GPU缓冲区表征为安全和非安全,StrongBox减少了冗余的安全自省操作,以控制对GPU使用的敏感数据的访问,最终减少了运行时开销。我们的设计利用了广泛部署的Arm TrustZone和通用的Arm功能,无需硬件修改或架构更改。我们使用现成的Arm Mali GPU对StrongBox进行原型设计,并进行了广泛的评估。我们的结果表明,在几个指示性基准测试中,StrongBox以较低的开销(4.70% - 15.26%)成功地确保了GPU计算的安全性。
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