Discerning Limitations of GNN-based Attacks on Logic Locking

Machine learning (ML)-based attacks have revealed the possibility of utilizing neural networks to break locked circuits without needing functional chips (Oracle). Among ML approaches, GNN (graph neural networks)-based attacks are the most potent tools that attackers can employ as they exploit graph structures inherent to a circuit's netlist. Although promising, in this paper, we reveal that GNNs have some impediments in attacking locked circuits. We investigate the limits of the state-of-the-art GNN-based attacks against logic locking and show that we can drastically decrease the accuracy of these attacks by utilizing these limitations in the locking process.