Blackknife: Query-Free Black-Box Rewiring Attacks on Heterogeneous Graph Neural Networks
Read the original on arXiv Machine Learning →Blackknife is a query‑free, strict black‑box evasion attack designed for heterogeneous graph neural networks (HGNNs). It relies solely on observing one‑hop structures, ground‑truth labels, and raw features of target nodes, without using victim‑model outputs. The attack trains a shared surrogate on observable edges and relation types, then optimizes structural perturbations via continuous relaxation and projected gradient ascent, preserving node degrees while adding a cosine‑similarity penalty and adaptive structural classifier for regularization. Experiments show competitive success rates across multiple victim architectures and defense settings, revealing HGNN vulnerabilities under restricted graph access and query‑free attack generation.
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