2 research outputs found
Threshold Breaker: Can Counter-Based RowHammer Prevention Mechanisms Truly Safeguard DRAM?
This paper challenges the existing victim-focused counter-based RowHammer
detection mechanisms by experimentally demonstrating a novel multi-sided fault
injection attack technique called Threshold Breaker. This mechanism can
effectively bypass the most advanced counter-based defense mechanisms by
soft-attacking the rows at a farther physical distance from the target rows.
While no prior work has demonstrated the effect of such an attack, our work
closes this gap by systematically testing 128 real commercial DDR4 DRAM
products and reveals that the Threshold Breaker affects various chips from
major DRAM manufacturers. As a case study, we compare the performance
efficiency between our mechanism and a well-known double-sided attack by
performing adversarial weight attacks on a modern Deep Neural Network (DNN).
The results demonstrate that the Threshold Breaker can deliberately deplete the
intelligence of the targeted DNN system while DRAM is fully protected.Comment: 7 pages, 6 figure
DNN-Defender: An in-DRAM Deep Neural Network Defense Mechanism for Adversarial Weight Attack
With deep learning deployed in many security-sensitive areas, machine
learning security is becoming progressively important. Recent studies
demonstrate attackers can exploit system-level techniques exploiting the
RowHammer vulnerability of DRAM to deterministically and precisely flip bits in
Deep Neural Networks (DNN) model weights to affect inference accuracy. The
existing defense mechanisms are software-based, such as weight reconstruction
requiring expensive training overhead or performance degradation. On the other
hand, generic hardware-based victim-/aggressor-focused mechanisms impose
expensive hardware overheads and preserve the spatial connection between victim
and aggressor rows. In this paper, we present the first DRAM-based
victim-focused defense mechanism tailored for quantized DNNs, named
DNN-Defender that leverages the potential of in-DRAM swapping to withstand the
targeted bit-flip attacks. Our results indicate that DNN-Defender can deliver a
high level of protection downgrading the performance of targeted RowHammer
attacks to a random attack level. In addition, the proposed defense has no
accuracy drop on CIFAR-10 and ImageNet datasets without requiring any software
training or incurring additional hardware overhead.Comment: 10 pages, 11 figure