🤖 AI Summary
为解决跨链交易追踪难题,本文提出XSplicer系统,通过公开协议文档和交易示例重构跨链交易对应关系,实现高效准确的非法资金追踪。
📝 Abstract
Cross-chain bridges enable interoperability, but they also break the transaction trails needed to trace illicit funds. Third-party investigators typically cannot access the source-to-destination mappings maintained by bridge backends, and our survey of 131 bridges finds that only 16.79% provide complete public tracking. Existing approaches depend on official APIs, EVM-specific assumptions, or fragile temporal heuristics, limiting their ability to trace transfers across heterogeneous ledgers. We present XSplicer, an evidence-driven system for reconstructing cross-chain transaction correspondence (xTCR) without privileged access to bridge backends. XSplicer derives unified semantic specifications from public protocol documentation and transaction examples, translates them into lightweight parsers and verifiers, and links source and destination transactions by prioritizing hard evidence and using soft clues only when necessary. We evaluate XSplicer on seven bridge protocols spanning EVM, Bitcoin, and Solana. XSplicer achieves 92.5% global recovery rate and up to 98.61% on individual protocols. Under adversarial noise, its hard-evidence verifier retains the correct match in 100% of tested cases, while soft-clue matching degrades as ambiguity increases. In two real-world case studies, XSplicer recovers more than 1,900 historical transaction pairs after Multichain ceased operations and identifies 754 illicit cross-chain transfers worth 105.6 million USD in the Bybit laundering incident. These results show that public protocol invariants can support practical cross-chain forensics without privileged bridge mappings.