HyperAttack: An Efficient Attack Framework for HyperDimensional Computing

HyperDimensional Computing (HDC) is emerging as a lightweight computational model for robust and efficient learning on resource-constrained hardware. Since HDC often runs on edge devices, the security challenge of HDC is a pressing issue confronting all the practitioners. Meanwhile, the security challenge of HDC's parameters stored in memory has not been well studied. In this work, we are the first to propose a novel HDC attack framework called HyperAttack, which can crush a robust HDC model (i.e., binary HDC) by maliciously flipping an extremely few amount of bits within its memory system (i.e., DRAM) that stores the associative memory. Since the bit-flip operation can be conducted by the well-known Row Hammer attack, HyperAttack maximizes the accuracy degradation with the minimum number of bit-flips by identifying the bits closely related to the classification accuracy of hyperdimensional vectors (stored in the associative memory as binary vectors) in HDC. The proposed HyperAttack is based on the concept of fuzzing, combining dimensional ranking and distributions of features in hypervectors to identify the bits to be flipped. Our evaluation shows that HyperAttack can successfully attack a binary HDC by flipping only 10% bits of hyperdimensional vectors to decrease top-1 accuracy from 90.9% to 10%, while randomly flipping merely degrades the accuracy by less than 2%.