Blackknife: Query-Free Black-Box Rewiring Attacks on Heterogeneous Graph Neural Networks
Blackknife is a query-free, strict black-box attack designed to evade heterogeneous graph neural networks (HGNNs). It works by first training a local surrogate model based only on observable one-hop structures and then using gradient descent to iteratively modify the graph structure. This process finds specific edge rewiring operations that cause the target node's prediction to change, proving HGNN vulnerabilities under strict structural access constraints.