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arXiv · 2508.20517

BridgeShield: Risk-Aware Graph Modeling for Cross-Chain Bridge Attack Detection

Dan Lin·Shunfeng Lu·Ziyan Liu·Jiajing Wu·Junyuan Fang·Jianzhong Su·Bowen Song·Qing Xia·Zibin Zheng
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Abstract

Cross-chain bridges enable asset and state transfers across heterogeneous blockchains, but their complex cross-domain interactions introduce new attack surfaces that are difficult to monitor using traditional single-chain analysis methods. Existing approaches often focus on isolated on-chain behaviors and fail to capture the multi-stage execution semantics of cross-chain transactions. This paper presents BridgeShield, a graph-based framework for detecting cross-chain bridge attacks through risk-aware modeling of cross-chain execution behaviors. BridgeShield reconstructs cross-chain behavior graphs from execution traces and event logs, capturing interactions across the source chain, off-chain relay components, and the destination chain. To highlight attack-relevant structures, the framework employs differential meta-path selection to identify execution patterns that exhibit structural deviations between normal and attack transactions, and hierarchical risk propagation to aggregate distributed risk signals across interaction stages. Experiments on real-world bridge incidents show that BridgeShield achieves an F1-score of 92.6% in cross-chain attack detection and consistently outperforms existing rule-based and graph-based baselines. In addition, the model remains effective in detecting previously unseen attack incidents, indicating that BridgeShield captures structural risk patterns rather than memorizing historical attack templates.

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Dan Lin, Shunfeng Lu, Ziyan Liu, Jiajing Wu, Junyuan Fang, Jianzhong Su, Bowen Song, Qing Xia, Zibin Zheng. 2026-09-20. BridgeShield: Risk-Aware Graph Modeling for Cross-Chain Bridge Attack Detection. https://arxiv.org/abs/2508.20517

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