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

arXiv: https://arxiv.org/abs/2508.20517 · HTML: https://mlsanity.com/paper/2508.20517/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [A Survey on Blockchain Interoperability: Past, Present, and Future Trends](https://mlsanity.com/paper/2005.14282/) (2020)
- [TSGN: Transaction Subgraph Networks Assisting Phishing Detection in Ethereum](https://mlsanity.com/paper/2208.12938/) (2022)
- [SoK: Not Quite Water Under the Bridge: Review of Cross-Chain Bridge Hacks](https://mlsanity.com/paper/2210.16209/) (2022)
- [Enhancing Smart Contract Security Analysis with Execution Property Graphs](https://mlsanity.com/paper/2305.14046/) (2023)
- [CONNECTOR: Enhancing the Traceability of Decentralized Bridge Applications via Automatic Cross-chain Transaction Association](https://mlsanity.com/paper/2409.04937/) (2024)
- [Xscope: Hunting for Cross-Chain Bridge Attacks](https://mlsanity.com/paper/2208.07119/) (2022)
- [Safeguarding Blockchain Ecosystem: Understanding and Detecting Attack Transactions on Cross-chain Bridges](https://mlsanity.com/paper/2410.14493/) (2024)
- [SoK: Security of Cross-chain Bridges: Attack Surfaces, Defenses, and Open Problems](https://mlsanity.com/paper/2312.12573/) (2023)
- [SoK: Cross-Chain Bridging Architectural Design Flaws and Mitigations](https://mlsanity.com/paper/2403.00405/) (2024)

## Abstract (arXiv)

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.