arXiv:2605.12827v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) deployed as cloud services can be stolen through model-extraction attacks, which train a surrogate from query responses to reproduce the target's behavior, and a growing line of ownership defenses tries to prevent or trace such theft. This paper asks two questions: how hard is it to steal a GNN, and can we stop it? Prior work cannot answer either, because experiments use inconsistent datasets, threat models, and metrics. We introduce GraphIP-Bench, a unified benchmark that evaluates both sides under a single
The proliferation of GNNs as cloud services and the increasing sophistication of AI models make their security and intellectual property protection a critical and timely concern.
The ability to protect proprietary AI models, especially GNNs which are crucial for complex data, directly impacts competitive advantage and national security in the AI domain.
This research introduces a standardized benchmark to rigorously assess the vulnerability of GNNs to theft and the effectiveness of defensive measures, enabling more consistent and reliable security evaluations.
- · AI defense companies
- · Cloud AI service providers implementing robust security
- · Researchers in AI security and intellectual property
- · Malicious actors performing model extraction
- · Cloud AI service providers with weak security protocols
- · Organizations relying on unprotected GNNs
Increased focus on model intellectual property protection for AI deployed in cloud environments.
Development and adoption of industry standards for AI model security and ownership verification.
The emergence of a distinct market for AI model security and intellectual property rights enforcement.
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