Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks
Graph Neural Networks (GNNs) have achieved remarkable results in various tasks. Recent studies reveal that graph backdoor attacks can poison the GNN model to predict test nodes with triggers attached as the target class. However, apart from injecting triggers to training nodes, these graph backdoor attacks generally require altering the labels of trigger-attached training nodes into the target class, which is impractical in real-world scenarios. In this work, we focus on the clean-label graph backdoor attack, a realistic but understudied topic where training labels are not modifiable. According to our preliminary analysis, existing graph backdoor attacks generally fail under the clean-lab...
| Authors | Yuxiang Zhang, Bin Ma, Enyan Dai |
|---|---|
| Primary category | cs.LG |
| Published | 5 March 2026 |
| Last updated at source | 14 April 2026 |
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