Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Leman Go Indifferent
Authors: 
Lorenz Kummer, Samir Moustafa, Sebastian Schrittwieser, Wilfried Gansterer, Nils KriegeAuthors Info & Claims
Pages 1428 - 1439
Abstract
Prior attacks on graph neural networks have focused on graph poisoning and evasion, neglecting the network's weights and biases. For convolutional neural networks, however, the risk arising from bit flip attacks is well recognized. We show that the direct application of a traditional bit flip attack to graph neural networks is of limited effectivity. Hence, we discuss 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 impairing the expressivity of the Weisfeiler-Leman test. We find that exploiting mathematical properties specific to certain graph neural networks significantly increases their vulnerability to bit flip attacks. The Injectivity Bit Flip Attack can degrade the maximal expressive Graph Isomorphism Networks trained on 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 traditional bit flip attacks transferred from convolutional neural networks. Our attack is transparent, motivated by theoretical insights and confirmed by extensive empirical results.
Supplemental Material
MP4 File - Attacking Graph Neural Networks with Bit Flips: Promotional Video
In our promotional Video, we introduce Graph Neural Network (GNN) expressivity as a potential security vulnerability that can be exploited by attackers using gradient-based bit-search methods. We illustrate that with our dedicated attack, GNNs can be degraded much faster than by attack methods ported from Convolutional Neural Networks (CNNs). Further, our attack solutions lead to distributions of bit flips across the network, which are distinct from existing attacks.In our promotional video, we introduce the expressivity of Graph Neural Networks (GNNs) as a potential security vulnerability exploitable by attackers using gradient-based bit-search methods. We demonstrate that our dedicated attack can degrade GNNs significantly faster than attacks adapted from Convolutional Neural Networks (CNNs). Additionally, our attack results in distinct distributions of bit flips across the network compared to existing methods.
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