Yes, One-Bit-Flip Matters! Universal DNN Model Inference Depletion with Runtime Code Fault Injection


Shaofeng Li, Peng Cheng Laboratory; Xinyu Wang, Shanghai Jiao Tong University; Minhui Xue, CSIRO's Data61; Haojin Zhu, Shanghai Jiao Tong University; Zhi Zhang, University of Western Australia; Yansong Gao, CSIRO's Data61; Wen Wu, Peng Cheng Laboratory; Xuemin (Sherman) Shen, University of Waterloo


We propose, FrameFlip, a novel attack for depleting DNN model inference with runtime code fault injections. Notably, Frameflip operates independently of the DNN models deployed and succeeds with only a single bit-flip injection. This fundamentally distinguishes it from the existing DNN inference depletion paradigm that requires injecting tens of deterministic faults concurrently. Since our attack performs at the universal code or library level, the mandatory code snippet can be perversely called by all mainstream machine learning frameworks, such as PyTorch and TensorFlow, dependent on the library code. Using DRAM Rowhammer to facilitate end-to-end fault injection, we implement Frameflip across diverse model architectures (LeNet, VGG-16, ResNet-34 and ResNet-50) with different datasets (FMNIST, CIFAR-10, GTSRB, and ImageNet). With a single bit fault injection, Frameflip achieves high depletion efficacy that consistently renders the model inference utility as no better than guessing. We also experimentally verify that identified vulnerable bits are almost equally effective at depleting different deployed models. In contrast, transferability is unattainable for all existing state-of-the-art model inference depletion attacks. Frameflip is shown to be evasive against all known defenses, generally due to the nature of current defenses operating at the model level (which is model-dependent) in lieu of the underlying code level.

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