这篇论文用简单的NVML遥测就能高精度检测隐藏的ML训练，还能对抗各种伪装，对AI计算治理很有启发。
该论文评估了使用零开销、保护隐私的NVML遥测（仅观察计算的物理效应，不访问模型权重或训练数据）进行GPU工作负载分类的对抗鲁棒性。研究人员在5轮监控-逃避迭代中，针对9种GPU模型（跨4代架构）评估了20种逃避策略。开发的分类器在全语料库上实现了98.2%的二进制准确率，面对最困难意外工作负载（即使被对抗性伪装）也能达到43-87%的准确率。
Detecting Hidden ML Training With Zero-Overhead Telemetry
Hardware-enabled monitoring of GPU workloads underpins many proposals for AI compute governance, but if developers can defeat monitoring mechanisms, such schemes are unworkable. We evaluate the adversarial robustness of GPU workload classification using only zero-overhead, privacy-preserving NVML telemetry: content-agnostic signals that observe physical effects of computation without accessing model weights, training data, or hyperparameters. Across 5 rounds of monitor-evader iteration, we evaluate 20 evasion strategy families on 9 GPU models spanning 4 architecture generations. We develop a classifier that achieves 98.2% binary accuracy at identifying training workloads across the whole corpus, and 43-87% accuracy against the most challenging unexpected workloads even when they are adversarially disguised.