VeriBridge: Real-Time Cross-Chain Bridge Attack Detection through Static-Informed Graph Anomaly Learning

Abstract

Cross-chain bridges, while critical for interoperability in the Web3 ecosystem, have become a primary target for exploits, accounting for over $4.3 billion in losses—nearly 40% of all value stolen in recent years. Existing security paradigms fail to address this threat adequately due to a fundamental trade-off between pre-deployment static analysis and real-time dynamic monitoring. Current security paradigms are trapped in a critical trade-off: pre-deployment static analysis lacks runtime context and suffers from high false-positives, while real-time dynamic monitoring is blind to the underlying source-code vulnerabilities that enable sophisticated attacks. This paper introduces VeriBridge, a novel framework that breaks this impasse. VeriBridge pioneers a synergistic fusion of static intelligence and dynamic graph learning. It enriches real-time transaction graphs with fine-grained vulnerability data extracted from static analysis, providing crucial security context to an unsupervised Graph Autoencoder. By learning a high-fidelity model of normal behavior, VeriBridge detects malicious transactions, including zero-day exploits, as significant deviations from this learned norm, identified by high reconstruction error. Evaluated on a comprehensive dataset of real-world attacks, including the Poly Network and THORchain exploits, VeriBridge achieves a 96% F1-score, demonstrating a new frontier in robust, real-time security for critical blockchain infrastructure.