AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling
Existing learning-based detectors for Solidity smart-contracts reduce vulnerability detection to syntactic pattern matching within single functions, yet many of the most consequential exploits (The DAO, Cream Finance) exist not in any individual function but in the relationship between functions and in the combination of conditions that made the attack feasible. Thus, we propose AttackPathGNN, a graph neural network (GNN) that reframes detection as reasoning over explicit attack paths. Two architectural choices distinguish it from prior GNN-based detectors: (1)a State Interference Graph that links every pair of functions sharing mutable storage through typed, weighted edges and through di...
| Authors | Gabriela Dobrita, Simona-Vasilica Oprea, Adela Bara |
|---|---|
| Primary category | cs.CR |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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