TracePilot: Self-Verifiable Framework for Decentralized Applications Fault Localization across Transactions

Abstract

Decentralized Applications (DApps) serve as a critical technical underpinning for business logic and user interaction within the blockchain-powered Web3 ecosystem. However, DApps are prone to faults, and localizing these faults within their intricate and often interconnected logic is a particularly time-consuming process, frequently taking tens of hours and leading to substantial economic losses for developers. Existing state-of-the-art DApp fault localization methods, e.g., FaultSeeker, cannot capture cross-transaction fault logic and produce verifiable diagnostic reports. Therefore, security experts have to spend substantial time manually verifying results and devising fixes. In this paper, we present TracePilot, a large language model (LLM)-based framework that automates DApp fault localization in two phases: distilling global fault insights from transaction sequences and then performing focused trace exploration to isolate the faulty logic. Crucially, we propose a patch verification mechanism that treats attack-blocking patches as executable evidence for fault localization while flagging potential overfitting risks for expert review. This mechanism improves result trustworthiness and reduces manual verification costs. Evaluated on a dataset of 149 real-world cases, TracePilot achieves a 71.14% Top-1 Recall. In the single-transaction fair comparison, it achieves 72.73%, substantially outperforming the state-of-the-art method at 32.23%. On cross-transaction cases, TracePilot achieves a 64.29% Top-1 Recall. The proposed algorithm is being integrated into the contract security agent developed by Ant Digital Technologies. Moreover, to facilitate further research, our code and dataset are publicly available online: https://github.com/feiqiuaaaa/TracePilot.