In this work, we conduct in-depth characterization to quantify the impact of DRAM refresh, the location of the target memory object within a non-uniform memory access (NUMA) node, and task and page placement across NUMA nodes and identify a set of the patterns in the clflush latency data. Based on characterization results, we propose MARF, a novel memory-aware clflush-based intra- and inter-CPU side-channel attack on NUMA systems. Our case studies on three real NUMA systems demonstrate that MARF can robustly be used to attack applications that use widely-used cryptographic and user-interface libraries. We also present potential countermeasures against MARF.
CITATION STYLE
Kim, S., Han, M., & Baek, W. (2024). MARF: A Memory-Aware CLFLUSH-Based Intra- and Inter-CPU Side-Channel Attack. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 14346 LNCS, pp. 120–140). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-51479-1_7
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