SNPeek：面向机密虚拟机隐私应用的侧信道分析
SNPeek: Side-Channel Analysis for Privacy Applications on Confidential VMs
Ruiyi Zhang, Albert Cheu, Adria Gascon, Daniel Moghimi, Phillipp Schoppmann, Michael Schwarz, Octavian Suciu
AI 速览：作者提出开源工具包 SNPeek，在生产级 AMD SEV-SNP 硬件上提供可配置的侧信道追踪原语，并结合统计与机器学习分析流程，自动估计隐私应用在机密虚拟机中的泄漏。
Confidential virtual machines (CVMs) based on trusted execution environments (TEEs) enable new privacy-preserving solutions. Yet, they leave side-channel leakage outside their threat model, shifting the responsibility of mitigating such attacks to developers. However, mitigations are either not generic or too slow for practical use, and developers currently lack a systematic, efficient way to measure and compare leakage across real-world deployments. In this paper, we present SNPeek, an open-source toolkit that offers configurable side-channel tracing primitives on production AMD SEV-SNP hardware and couples them with statistical and machine-learning-based analysis pipelines for automated leakage estimation. We apply SNPeek to three representative workloads that are deployed on CVMs to enhance user privacy-private information retrieval, private heavy hitters, and Wasm user-defined functions-and uncover previously unnoticed leaks, including a covert channel that exfiltrates data at 497 kbit/s. The results show that SNPeek pinpoints vulnerabilities and guides low-overhead mitigations based on oblivious memory and differential privacy, giving practitioners a practical path to deploy CVMs with meaningful confidentiality guarantees.