  Prior attacks on graph neural networks have mostly focused on graph poisoning
and evasion, neglecting the network's weights and biases. Traditional
weight-based fault injection attacks, such as bit flip attacks used for
convolutional neural networks, do not consider the unique properties of graph
neural networks. We propose the Injectivity Bit Flip Attack, the first bit flip
attack designed specifically for graph neural networks. Our attack targets the
learnable neighborhood aggregation functions in quantized message passing
neural networks, degrading their ability to distinguish graph structures and
losing the expressivity of the Weisfeiler-Lehman test. Our findings suggest
that exploiting mathematical properties specific to certain graph neural
network architectures can significantly increase their vulnerability to bit
flip attacks. Injectivity Bit Flip Attacks can degrade the maximal expressive
Graph Isomorphism Networks trained on various graph property prediction
datasets to random output by flipping only a small fraction of the network's
bits, demonstrating its higher destructive power compared to a bit flip attack
transferred from convolutional neural networks. Our attack is transparent and
motivated by theoretical insights which are confirmed by extensive empirical
results.
