📝 SummarXiv

Abstract

Rowhammer attacks have emerged as a significant threat to modern DRAM-based memory systems, leveraging frequent memory accesses to induce bit flips in adjacent memory cells. This work-in-progress paper presents an adaptive, many-sided Rowhammer attack utilizing GPU compute shaders to systematically achieve high-frequency memory access patterns. Our approach employs statistical distributions to optimize row targeting and avoid current mitigations. The methodology involves initializing memory with known patterns, iteratively hammering victim rows, monitoring for induced errors, and dynamically adjusting parameters to maximize success rates. The proposed attack exploits the parallel processing capabilities of GPUs to accelerate hammering operations, thereby increasing the probability of successful bit flips within a constrained timeframe. By leveraging OpenGL compute shaders, our implementation achieves highly efficient row hammering with minimal software overhead. Experimental results on a Raspberry Pi 4 demonstrate that the GPU-based approach attains a high rate of bit flips compared to traditional CPU-based hammering, confirming its effectiveness in compromising DRAM integrity. Our findings align with existing research on microarchitectural attacks in heterogeneous systems that highlight the susceptibility of GPUs to security vulnerabilities. This study contributes to the understanding of GPU-assisted fault-injection attacks and underscores the need for improved mitigation strategies in future memory architectures.