AxRA: Approximate Rowhammer Attack for Modern DRAM Systems
Yuhang Hao, Yun Wu, Ziying Ni, Jack Miskelly, Maire P. O'Neill, Chongyan Gu · 2025
Approximate computing achieves high performance or less power consumption in various fault-tolerant applications, e.g., image processing, artificial intelligence (AI), etc. However, the introduction of approximate computing brings new security vulnerabilities, which threaten the entire computing system. In this paper, a novel Rowhammer attack is proposed, which utilises the approximate data stored in DRAM memories to achieve higher attack effectiveness. Compared to Rowhammer attack to DRAM memory without approximate data, the proposed method achieves more bit-flips resulting in significant data corruption. The proposed attack is implemented and evaluated on DRAM chips with a real user case, object detection using neural network. The accuracy of detection on the baseline image is employed to verify the impact of proposed attack approach. The results show that the proposed Rowhammer attack with approximate data introduces extra 33% bit-flips on victim rows than a conventional Rowhammer attack without approximate data. It also introduces up to ∼75% accuracy reduction of MNIST neural network proportionally to the increment of attack activation number.