Graph Neural Networks (GNNs) have shown great success in learning on graph-structured data, such as social networks, recommendation systems, and protein-protein interactions. In our work, we leverage the fact that circuits can be represented naturally as graphs and we employ GNNs to learn the properties of circuits. Through our graph-based learning on circuits, we are able to identify critical security vulnerabilities in the implementations of various design-for-trust solutions aimed to achieve hardware security.

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