论文标题
SOK:关于云中多租户FPGA的安全挑战和风险
SoK: On the Security Challenges and Risks of Multi-Tenant FPGAs in the Cloud
论文作者
论文摘要
在持续的增长和渗透到新市场中,现场可编程门阵列(FPGA)最近在云数据中心(例如亚马逊和微软)的其他专业计算密集型服务中,在机器学习的硬件加速方面进入了硬件加速。为了进一步最大化其在云中的利用,几项学术著作提出了空间多租户部署模型,在该模型中,FPGA面料在相互不信任的客户中同时共享。通过利用FPGA的部分重新配置属性来启用这,该属性允许将FPGA织物拆分为几个逻辑隔离区域并在运行时独立地重新配置每个区域的功能。在本文中,我们调查了云计算设置中多租户FPGA的工业和学术部署模型,并强调了他们不同的对手模型和安全保证,同时从安全角度来看了他们的基本缺点。我们进一步调查并对现有的学术工作进行了分类,这些工作表明了对多租户FPGA设备的新一类远程可利用的物理攻击,这些攻击是由恶意客户远程发起的,这些攻击是与受害者用户共享物理资源的远程发射。通过更全面地调查端到端多租户FPGA部署的问题,我们揭示了这些攻击实际上仅代表问题的一个维度,而各种开放的安全性和隐私挑战仍然没有解决。我们以见解和呼吁将来的研究来应对这些挑战。
In their continuous growth and penetration into new markets, Field Programmable Gate Arrays (FPGAs) have recently made their way into hardware acceleration of machine learning among other specialized compute-intensive services in cloud data centers, such as Amazon and Microsoft. To further maximize their utilization in the cloud, several academic works propose the spatial multi-tenant deployment model, where the FPGA fabric is simultaneously shared among mutually mistrusting clients. This is enabled by leveraging the partial reconfiguration property of FPGAs, which allows to split the FPGA fabric into several logically isolated regions and reconfigure the functionality of each region independently at runtime. In this paper, we survey industrial and academic deployment models of multi-tenant FPGAs in the cloud computing settings, and highlight their different adversary models and security guarantees, while shedding light on their fundamental shortcomings from a security standpoint. We further survey and classify existing academic works that demonstrate a new class of remotely exploitable physical attacks on multi-tenant FPGA devices, where these attacks are launched remotely by malicious clients sharing physical resources with victim users. Through investigating the problem of end-to-end multi-tenant FPGA deployment more comprehensively, we reveal how these attacks actually represent only one dimension of the problem, while various open security and privacy challenges remain unaddressed. We conclude with our insights and a call for future research to tackle these challenges.