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CCS2023顶会

FetchBench: Systematic Identification and Characterization of Proprietary Prefetchers

Till Schlüter, Amit Choudhari, Lorenz Hetterich, Leon Trampert, Hamed Nemati, Ahmad Ibrahim, Michael Schwarz, Christian Rossow, Nils Ole Tippenhauer

2023年份
11被引次数
9顶会引用

摘要

Prefetchers speculatively fetch memory using predictions on future memory use by applications. Different CPUs may use different prefetcher types, and two implementations of the same prefetcher can differ in details of their characteristics, leading to distinct runtime behavior. For a few implementations, security researchers showed through manual analysis how to exploit specific prefetchers to leak data. Identifying such vulnerabilities required tedious reverse-engineering, as prefetcher implementations are proprietary and undocumented. So far, no systematic study of prefetchers in common CPUs is available, preventing further security assessment. In this work, we address the following question: How can we systematically identify and characterize under-specified prefetchers in proprietary processors? To answer this question, we systematically analyze approaches to prefetching, design cross-platform tests to identify and characterize prefetchers on a given CPU, and demonstrate that our implementation FetchBench can characterize prefetchers on 19 different ARM and x86-64 CPUs. For example, FetchBench uncovers and characterizes a previously unknown replay-based prefetcher on the ARM Cortex-A72 CPU. Based on these findings, we demonstrate two novel attacks that exploit this undocumented prefetcher as a side channel to leak secret information, even from the secure TrustZone into the normal world. CCS CONCEPTS • Security and privacy → Side-channel analysis and countermeasures.

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