Do Explanations Increase the Risk of Decision Logic Leakage? Explanation-Guided Stealing of Graph Models
arXiv:2506.03087v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become essential tools for analyzing graph-structured data in domains such as drug discovery and financial analysis, leading to a growing demand for model transparency. Recent advances in explainable GNNs have addressed this need by revealing important subgraphs that influence predictions, but these explanation mechanisms may inadvertently expose these models to security risks. This paper investigates how such explanations potentially leak critical decision logic that can be exploited for model stealing. We p
The increasing deployment of sophisticated AI models and the concurrent demand for transparency are making the security implications of such transparency more apparent and urgent.
This research highlights a critical vulnerability in explainable AI, suggesting that efforts to increase model transparency might inadvertently expose proprietary decision logic to theft, impacting competitive advantage and security.
The understanding of AI security expands beyond traditional adversarial attacks to include risks inherent in explanation mechanisms, requiring a re-evaluation of how models are explained and deployed.
- · AI security researchers
- · Model stealing defence developers
- · Organizations prioritizing proprietary model protection
- · Developers of explainable GNNs without security considerations
- · Organizations reliant on proprietary GNNs for critical functions
- · Competitors with less robust intellectual property protection
The adoption of explainable AI (XAI) tools may be slowed or refined to include robust security measures against decision logic leakage.
New standards and best practices for secure XAI development and deployment will emerge, influencing regulatory frameworks.
The trade-off between AI transparency and security could become a defining challenge for advanced AI systems, potentially leading to 'security through obscurity' for highly sensitive models if secure XAI solutions are not found.
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