Kernekoncepter
Cross-chain bridges, while crucial for blockchain interoperability, are highly vulnerable to attacks, demanding robust detection tools like BridgeGuard, which leverages transaction graph analysis to identify and categorize attack patterns.
Resumé
Bibliographic Information:
Wu, J., Lin, K., Lin, D., Zhang, B., Wu, Z., & Su, J. (2025). Safeguarding Blockchain Ecosystem: Understanding and Detecting Attack Transactions on Cross-chain Bridges. In Conference’17 (pp. 1–14). ACM. https://doi.org/XXXXXXX.XXXXXXX
Research Objective:
This research paper aims to analyze the security risks associated with cross-chain bridges, understand the attack patterns targeting their business logic, and develop an effective tool for detecting such attacks.
Methodology:
The researchers collected data on 49 real-world cross-chain bridge attack incidents from June 2021 to September 2024. They analyzed these incidents to understand the attack patterns and developed BridgeGuard, a tool that models cross-chain transactions as graphs and employs global and local graph mining techniques to detect anomalies indicative of attacks. The tool was evaluated on a dataset of 203 attack transactions and 40,000 normal transactions.
Key Findings:
- Attacks targeting cross-chain business logic result in significantly higher financial losses compared to other attack types.
- Attack transactions exhibit distinct patterns in their call structure and event triggering compared to normal transactions.
- BridgeGuard achieved a recall of 80% in detecting attack transactions, outperforming existing tools like XScope and DeFiScanner.
- BridgeGuard also identified previously undetected attack transactions in known incidents.
Main Conclusions:
- Cross-chain bridge security is a critical concern due to the high financial stakes and the evolving nature of attack strategies.
- Analyzing transaction execution graphs is an effective approach for detecting attacks targeting cross-chain business logic.
- BridgeGuard provides a promising solution for enhancing the security of cross-chain bridges by accurately identifying and categorizing attack transactions.
Significance:
This research contributes significantly to the field of blockchain security by providing a comprehensive analysis of cross-chain bridge attacks and proposing an effective detection tool. The findings and the tool itself have practical implications for developers and security researchers working on securing cross-chain infrastructure.
Limitations and Future Research:
The study primarily focuses on attacks targeting cross-chain business logic and may not encompass all possible attack vectors. Future research could explore the applicability of BridgeGuard to other types of cross-chain bridges and investigate the use of large language models (LLMs) for enhanced attack detection.
Statistik
Attacks on cross-chain bridges have resulted in losses of nearly 4.3 billion dollars since 2021.
The researchers collected 49 cross-chain bridge attack incidents between June 2021 and September 2024.
Financial losses caused by attacks against cross-chain business logic were nearly six times greater than those from non-cross-chain business logic attacks.
65.7% of attack transactions cannot be linked to corresponding deposit or withdrawal transactions on the target or source chain.
BridgeGuard's recall is 36.32% higher than that of state-of-the-art tools.
BridgeGuard's final transactions per second (TPS) reached 65 transactions.
Citater
"These cross-chain attacks exhibit different patterns compared to normal transactions in terms of call structure, which effectively indicates potential attack behaviors."
"BridgeGuard’s reported recall score is 36.32% higher than that of state-of-the-art tools and can detect unknown attack transactions."