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arXiv · 2511.01393

ConneX: Automatically Resolving Transaction Opacity of Cross-Chain Bridges for Security Analysis

Hanzhong Liang·Yue Duan·Xing Su·Xiao Li·Yating Liu·Yulong Tian·Fengyuan Xu·Sheng Zhong
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Abstract

As the Web3 ecosystem evolves toward a multi-chain architecture, cross-chain bridges have become critical infrastructure for enabling interoperability between diverse blockchain networks. However, while connecting isolated blockchains, the lack of cross-chain transaction pairing records introduces significant challenges for security analysis like cross-chain fund tracing, advanced vulnerability detection, and transaction graph-based analysis. To address this gap, we introduce ConneX, an automated and general-purpose system designed to accurately identify corresponding transaction pairs across both ends of cross-chain bridges. Our system leverages Large Language Models (LLMs) to efficiently prune the semantic search space by identifying semantically plausible key information candidates within complex transaction records. Further, it deploys a novel examiner module that refines these candidates by validating them against transaction values, effectively addressing semantic ambiguities and identifying the correct semantics. Extensive evaluations on a dataset of about 500,000 transactions from five major bridge platforms demonstrate that ConneX achieves an average F1 score of 0.9746, surpassing baselines by at least 20.05\%, with good efficiency that reduces the semantic search space by several orders of magnitude (1e10 to less than 100). Moreover, its successful application in tracing illicit funds (including a cross-chain transfer worth $1 million) in real-world hacking incidents underscores its practical utility for enhancing cross-chain security and transparency.

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Hanzhong Liang, Yue Duan, Xing Su, Xiao Li, Yating Liu, Yulong Tian, Fengyuan Xu, Sheng Zhong. 2025-11-03. ConneX: Automatically Resolving Transaction Opacity of Cross-Chain Bridges for Security Analysis. https://arxiv.org/abs/2511.01393

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