arXiv:2503.00065v4 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) achieve high performance in various real-world applications, such as drug discovery, traffic states prediction, and recommendation systems. The fact that building powerful GNNs requires a large amount of training data, powerful computing resources, and human expertise turns the models into lucrative targets for model stealing attacks. Prior work has revealed that the threat vector of stealing attacks against GNNs is large and diverse, as an attacker can leverage various heterogeneous signals ranging from nod
The increasing prevalence and power of Graph Neural Networks (GNNs) across critical applications makes their vulnerabilities to model stealing a pressing concern for current AI development and deployment.
Sophisticated readers should care because the security and intellectual property of AI models, especially GNNs, are crucial for their economic value and trust in real-world applications.
This research introduces active defense mechanisms, shifting the focus from purely reactive security measures to proactive deterrence against GNN model extraction attacks.
- · AI developers and deployers
- · Cybersecurity firms
- · Intellectual property rights holders
- · High-value data industries
- · Malicious actors
- · Model thieves
- · Organizations with weak AI security
- · Researchers reliant on stolen models
Increased robustness and security of GNN-powered applications across various sectors.
A potential rise in the economic value of proprietary AI models, as they become harder to steal.
Enhanced trust in AI systems leading to broader adoption in sensitive domains such as finance, healthcare, and national security.
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Read at arXiv cs.LG