Inference-Engine Fingerprinting Attacks Are Practical

New research demonstrates that inference engines powering black-box machine learning APIs can be practically fingerprinted. By exploiting observable execution anomalies, timing signatures, and scheduling behaviors across different serving runtimes, external queries can reveal the underlying backend stack. The work surfaces a subtle infrastructure-level side channel in commercial model deployment.