Solana bridges traced reliably despite missing logs and complex transactions
Heterogeneous Cross-Chain Transaction Tracing for Solana Bridges via Candidate-Set Selective Decision
Cryptography and Security
Summary
Cross-chain bridges let users move digital money between blockchains, but criminals exploit them to hide where funds come from. Solana's blockchain works differently than others, making it hard to track transactions using existing tools. The authors designed a new method called SolTracer that better matches transactions across blockchains by understanding Solana's unique details. Their tests show SolTracer traces transactions more accurately than previous methods.
What this means in practice
- •For blockchain security teams: Identify illicit fund transfers on Solana by tracing cross-chain bridge transactions more accurately to improve compliance.
- •For cryptocurrency analysts: Analyze cross-chain transfer patterns and ecosystem dynamics on Solana with enhanced transaction lineage insights.
Authors
Wenjie Dou, Zheng Che, Meng Shen, Hanbiao Du, Qing Li, Yan Qiang
Abstract
Solana is a rapidly growing high-throughput blockchain platform that has attracted substantial liquidity and user activity. However, this expansion has also drawn the attention of illicit actors, who frequently leverage cross-chain bridges to route illicit funds onto Solana to obfuscate transaction lineage. Unlike EVM-compatible platforms, Solana features distinct execution dynamics and lacks standard event logs, creating severe semantic gaps that prevent existing tracing methods from reliably correlating cross-ledger transactions. In this paper, we formalize four types of Solana-bound cross-chain transaction modes and propose a candidate-set selective decision-based tracing method called SolTracer. SolTracer maps disparate execution semantics into a unified event space and employs candidate-set selective decision-making to reliably associate target transactions while abstaining when valid targets are absent. Extensive experiments demonstrate that SolTracer outperforms state-of-the-art (SOTA) methods across three representative scenarios: closed-world association, open-world association, and cross-source-chain generalization. In particular, under the challenging open-world setting with a 50% TA ratio, SolTracer improves the F1 score by 20.16% over the strongest SOTA baseline. Utilizing SolTracer, we conduct an empirical analysis on real-world cross-chain transfers to investigate ecosystem dynamics. Our analysis explores the stark count-value divergence across bridge mechanisms, the prevalence of cross-asset shifts, and the decoupling between on-chain settlement and explorer visibility.