Detecting model weight tampering in GPU memory (Rome attacks)
Researchers have introduced a method to identify unauthorized modifications of neural‑network weights stored in GPU memory. By analyzing memory patterns and employing integrity checks, the approach can spot the so‑called “Rome” attacks that aim to alter model parameters during execution. Experiments show the technique detects tampering with low overhead, offering a potential safeguard for AI deployments on GPUs.