SLAC: Access-Driven CPU-to-GPU Side-channel Attacks via System-Level Cache on Apple Silicon

• URL: http://arxiv.org/abs/2608.09075v2
• Date: Fri, 14 Aug 2026 00:51:41 GMT
• Title: SLAC: Access-Driven CPU-to-GPU Side-channel Attacks via System-Level Cache on Apple Silicon
• Abstract summary: heterogeneous System-on-Chip designs integrate CPU cores and a GPU that share a last-level cache (LLC) or system-level cache (SLC)<n>This sharing exposes a new cross-domain attack surface, and existing attacks on integrated platforms either exploit coarse-grained cache-occupancy contention or require the adversary to co-reside on the GPU with the victim to obtain accurate timing measurements.<n>In this work, we target Apple Silicon heterogeneous SOCs and discover that GPU memory accesses leave set-level footprints in the shared SLC, observable to an unprivileged CPU process.<n>This keen observation enables the first fine-grained
• Score: 8.388281067288181
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern heterogeneous System-on-Chip designs integrate CPU cores and a GPU that share a last-level cache (LLC) or system-level cache (SLC). This sharing exposes a new cross-domain attack surface, and existing attacks on integrated platforms either exploit coarse-grained cache-occupancy contention or require the adversary to co-reside on the GPU with the victim to obtain accurate timing measurements. In this work, we target Apple Silicon heterogeneous SoCs and discover that GPU memory accesses leave set-level footprints in the shared SLC, observable to an unprivileged CPU process. This keen observation enables the first fine-grained, access-driven, Prime+Probe-style CPU-to-GPU cache side-channel attacks against GPU workloads. We first reverse-engineer the Apple M1 SLC set-indexing functions and the interactions between local private caches and the SLC. Building on these findings, we construct the CPrime+CProbe SLC side-channel technique, which monitors GPU victim activity from the CPU at cache-set granularity. We then introduce an accelerated variant, GPrime+CProbe, in which an adversary leverages the GPU for faster SLC priming, yielding a 6.4x increase in the covert-channel throughput. Lastly, we demonstrate two end-to-end privacy attacks using the new side-channels: a graph-edge reconstruction attack on Graph Neural Networks (GNNs) that achieves 90% edge accuracy across five datasets, and an LLM privacy attack that recovers input keywords with up to 94.8% accuracy and model responses with up to 88.9% accuracy across TinyLlama and GPT-2 Medium models. Our results reveal a new class of microarchitectural vulnerabilities in Apple Silicon and call for secure system cache designs for heterogeneous SoCs.

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