一句话:首次系统研究公有云 FaaS 中攻击者与受害函数实例的物理主机共置风险,并提出高效共置攻击技术。
微架构侧信道攻击通常要求攻击者与受害者在同一物理主机上,但公有云中的 VPC、数据中心规模以及 FaaS 的动态实例放置使共置难以实现。本文首次全面研究公共云 FaaS 环境中的共置攻击风险与技术,开发了两种物理主机指纹识别方法,并提出一种低成本的大规模实例共置验证方法。作者利用这些技术分析 Google Cloud Run 如何将函数实例放置到物理主机上,识别出可利用的放置行为,并据此设计实例启动策略。评估表明,该策略能以 100% 概率使攻击者与至少一个受害实例共置,并在三个主要 Cloud Run 数据中心中与 61%--100% 的受害实例共置。
Microarchitectural side-channel attacks exploit shared hardware resources, posing significant threats to modern systems. A pivotal step in these attacks is achieving physical host co-location between attacker and victim. This step is especially challenging in public cloud environments due to the widespread adoption of the virtual private cloud (VPC) and the ever-growing size of the data centers. Furthermore, the shift towards Function-as-a-Service (FaaS) environments, characterized by dynamic function instance placements and limited control for attackers, compounds this challenge. In this paper, we present the first comprehensive study on risks of and techniques for co-location attacks in public cloud FaaS environments. We develop two physical host fingerprinting techniques and propose a new, inexpensive methodology for large-scale instance co-location verification. Using these techniques, we analyze how Google Cloud Run places function instances on physical hosts and identify exploitable placement behaviors. Leveraging our findings, we devise an effective strategy for instance launching that achieves 100% probability of co-locating the attacker with at least one victim instance. Moreover, the attacker co-locates with 61%--100% of victim instances in three major Cloud Run data centers.