University of Toronto researchers devise Rowhammer attack for GPUs. This was until recently only possible for CPUs.

The Rowhammer technique, a hardware vulnerability known for more than a decade, works by repeatedly accessing — or “hammering” — a specific row of DRAM memory cells. This rapid activity can generate electrical interference that causes bit flips in neighboring memory regions. Over the years, researchers have shown that Rowhammer attacks can be exploited to enable privilege escalation, unauthorized data access, data corruption, and breaches of memory isolation in virtualized environments. Until recently, however, such attacks had been limited to CPUs and traditional CPU-based memory. With GPUs playing an increasingly critical role in AI and machine learning workloads, a team from the University of Toronto successfully demonstrated a Rowhammer-style attack targeting the memory of an Nvidia GPU. They showed how the attack, dubbed GPUHammer, can induce bit flips that significantly degrade the accuracy of deep neural network (DNN) models, including ImageNet-trained models used for visual object recognition. The researchers behind GPUHammer, assisted by several others, have now demonstrated that GPU Rowhammer attacks can be used for more than just disruption. Their new attack, named GPUBreach, shows that attackers can induce GDDR6 bit flips that corrupt GPU page tables, enabling arbitrary read-write access to memory. In combination with new memory-safety bugs in Nvidia drivers, the researchers showed that GPUBreach can be used for CPU-side privilege escalation, ultimately achieving root shell privileges and full system compromise. The attack can pose a significant threat to cloud environments, where multiple users share the same physical GPU. Reported in April 2026 submitted by /u/Choobeen [link] [comments]

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