(A)iSpy: Parasitic Trojans for Machine Learning Infrastructure
A new preprint introduces (A)iSpy, a parasitic Trojan that targets machine learning infrastructure by embedding itself within the computation graph. The Trojan can monitor tensor states, exfiltrate sensitive hyperparameters, and amplify data poisoning into highly effective backdoor attacks, all while evading standard malware detection. The attack is demonstrated on ONNX Runtime training and inference engines, highlighting a critical vulnerability in the ML execution environment.
Why it matters: This work reveals a significant and previously underexplored security risk in ML infrastructure, showing that malicious runtime modules can achieve manipulations beyond the reach of traditional data or model-level attacks.
Full story at: arXiv Cryptography and Security ↗