GPUThor: Amplifying Rowhammer Attacks via Non-Uniform Patterns to Exploit ECC-Protected GPUs
cs.CR
Submitted: 2026-09-15
Updated: 2026-09-15
Comments: 17 pages, including appendices. The paper will appear in CCS'26
Code: https://github.com/ilya-zlobintsev/LACT
License: http://creativecommons.org/licenses/by/4.0/
The gist: GDDR memory in GPUs is vulnerable to Rowhammer attacks, where rapid memory accesses induce bit flips in adjacent cells, enabling data tampering and privilege escalation.
Terminology
Abstract
GDDR memory in GPUs is vulnerable to Rowhammer attacks, where rapid memory accesses induce bit flips in adjacent cells, enabling data tampering and privilege escalation. However, prior GPU Rowhammer attacks trigger only tens to hundreds of bit flips, orders of magnitude fewer than CPU attacks, severely limiting their practical impact. This gap stems from the reliance of existing GPU Rowhammer attacks on uniform hammering patterns that activate aggressor and decoy rows equally, which results in low hammering intensity for aggressor rows. We present GPUThor, a high-intensity Rowhammer attack on NVIDIA GPUs leveraging non-uniform hammering. GPUThor reverse engineers GPU memory-access coalescing behavior to enable non-uniform hammering patterns on GPUs, that activate aggressor rows more intensely than decoy rows. Additionally, by identifying refresh instances when in-DRAM mitigations are applied, it constructs longer attack patterns that escape mitigation across refresh intervals, further increasing hammering intensity. Together, these techniques yield 500X to 23,500X more bit flips than prior GPU Rowhammer attacks, across several NVIDIA GPUs (A4000, A4500, A5000, A6000), reaching bit flip rates close to state-of-the-art CPU Rowhammer attacks. GPUThor also enables the first Rowhammer exploits on ECC-protected GPUs, inducing uncorrectable double and triple bit flips, making denial-of-service and privilege-escalation attacks practical even on GPUs with ECC enabled.
Sources
- PrisonBreak: Jailbreaking Large Language Models with at Most Twenty-Five Targeted Bit-flips
- Blueprint, Bootstrap, and Bridge: A Security Look at NVIDIA GPU Confidential Computing
- PVAC: A RowHammer Mitigation Architecture Exploiting Per-victim-row Counting
- LeftoverLocals: Listening to LLM Responses Through Leaked GPU Local Memory
- NVBleed: Covert and Side-Channel Attacks on NVIDIA Multi-GPU Interconnect
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