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The title of this paper asks two questions: \\emph{how hard is it to steal a GNN?}, and \\emph{can we stop it?} Prior work cannot answer either, because experiments use inconsistent datasets, threat models, and metrics. We introduce \\emph{GraphIP-Bench}, a unified benchmark which evaluates both sides under a single black-box protocol. It integrates twelve extraction attacks, twelve defenses spanning watermarking, output-perturbation, and query-pattern-detection families, ten public graphs covering homophilic, heterophilic, and large-scale regimes, three GNN backbones, and three graph-learning tasks, and it reports fidelity, task utility, ownership verification, an","title":"GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?","url":"https://arxiv.org/abs/2605.12827","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.12827v1 Announce Type: cross \nAbstract: Graph neural networks (GNNs) deployed as cloud services can be \\emph{stolen} through \\emph{model-extraction attacks}, which train a surrogate from query responses to reproduce the target's behaviour, and a growing line of ownership defenses tries to prevent or trace such theft. The title of this paper asks two questions: \\emph{how hard is it to steal a GNN?}, and \\emph{can we stop it?} Prior work cannot answer either, because experiments use inconsistent datasets, threat models, and metrics. We introduce \\emph{GraphIP-Bench}, a unified benchmark which evaluates both sides under a single black-box protocol. 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