GPU Rowhammer Is Real: A Single Bit Flip Drops AI Model Accuracy from 80% to 0.1%
A single bit flip in GPU memory dropped an AI model's accuracy from 80% to 0.1%.
That is not a theoretical risk. It is a documented, reproducible attack called GPUHammer, demonstrated on an NVIDIA RTX A6000 by University of Toronto researchers and presented at USENIX Security 2025. The attack requires only user-level CUDA privileges and works in multi-tenant cloud GPU environments where attacker and victim share the same physical GPU.
GPUHammer is not the only GPU hardware vulnerability. LeftoverLocals (CVE-2023-4969) proved that AMD, Apple, and Qualcomm GPUs leak memory between processes, allowing full reconstruction of LLM responses. NVBleed demonstrated cross-VM data leakage through NVIDIA's NVLink interconnect on Google Cloud Platform. And at RSA Conference 2026, analysts highlighted that traditional security tools monitor only CPU and OS activity, leaving GPU operations completely invisible.
If you are training or running inference on cloud GPUs, this matters. Here is the full technical breakdown.