The Power of Many: Investigating Defense Mechanisms for Resilient Graph Neural Networks

As Graph Neural Networks (GNNs) become widely used in critical industries like healthcare, finance, and entertainment, they are increasingly targeted by backdoor attacks. These attacks compromise GNN safety and reliability by subtly manipulating training data to introduce hidden vulnerabilities. This study assesses the effectiveness of state-of-the-art defense mechanisms against the most recent backdoor attack techniques, aiming to identify potential attacks that could bypass current detection strategies. By comparing attack success rates across various GNN architectures, we find that no single attack evades all defenses. However, a robust defense can be achieved by combining two major types of existing defense mechanisms. This result highlights the urgent need for stronger, more comprehensive defenses to support safe GNN deployment.