One Trigger, Multiple Victims: Clean-Label Neighborhood Backdoor Attacks on Graph Neural Networks

Researchers discovered a new backdoor attack (a security flaw where hidden malicious code is planted in training data) on Graph Neural Networks, or GNNs (AI models designed to understand interconnected data). The attack uses a single trigger node (a specially crafted fake data point) attached to a target node to trick the GNN into making wrong predictions not just on that node, but also on its immediate neighbors, while remaining stealthy and achieving over 95% success rates even against existing defenses.