Workload Identification with Physical Side Channels for AI Governance
AI compute verification is one of the first tangible and tractable points for international policy aimed at AI governance. Determining whether frontier labs, or any operator, comply with agreements requires the regulating authority to discern how their compute is used. The elementary building block of AI compute is the GPU, and any activity it executes leaves a physical trace. Here, we show that an external observer can identify the class of the workload running on an NVIDIA H200 from its power draw. Unlike on-chip NVML telemetry, which can be spoofed or replayed, such a physical channel can in principle be observed independently of operator cooperation. We recorded $930$ five-second trac...
| Authors | Simone Gargiulo, Gabriel Kulp |
|---|---|
| Primary category | cs.CR |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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