AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling
arXiv:2606.05986v1 Announce Type: cross Abstract: 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-bas
The increasing prevalence of sophisticated exploits in smart contracts and the limitations of existing syntactic detection methods necessitate more advanced, relational vulnerability analysis.
This research introduces a novel approach to securing smart contracts, moving beyond simple pattern matching to detect complex, multi-function attack vectors, which is critical for financial and logistical systems reliant on blockchain.
The focus shifts from individual function vulnerabilities to inter-function relationships and attack paths, potentially leading to more robust and secure smart contract development and auditing practices.
- · Blockchain platforms
- · Smart contract developers
- · Cybersecurity firms
- · DeFi ecosystem
- · Cyber attackers
- · Vulnerable smart contracts
- · Legacy security tools
Increased integrity and trust in smart contract operations due to enhanced vulnerability detection.
Reduced financial losses from smart contract exploits, fostering greater adoption of blockchain technology in high-value applications.
The methodology could be adapted to other complex software systems, improving overall software supply chain security and reliability.
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Read at arXiv cs.AI