Phishing attacks in Web3 ecosystems are increasingly sophisticated, exploiting deceptive contract logic, malicious frontend scripts, and token approval patterns. We present DeepTx, a real-time transaction analysis system that detects such threats before user confirmation. DeepTx simulates pending transactions, extracts behavior, context, and UI features, and uses multiple large language models (LLMs) to reason about transaction intent. A consensus mechanism with self-reflection ensures robust and explainable decisions. Evaluated on our phishing dataset, DeepTx achieves high precision and recall (demo video: https://youtu.be/4OfK9KCEXUM).

翻译：Web3生态系统中的网络钓鱼攻击日益复杂，常利用欺骗性合约逻辑、恶意前端脚本及代币授权模式进行欺诈。本文提出DeepTx，一种实时交易分析系统，可在用户确认前检测此类威胁。DeepTx通过模拟待处理交易，提取行为、上下文及用户界面特征，并利用多个大型语言模型（LLM）对交易意图进行推理。结合自反思机制的共识算法确保了决策的鲁棒性与可解释性。在钓鱼攻击数据集上的评估表明，DeepTx实现了较高的精确率与召回率（演示视频：https://youtu.be/4OfK9KCEXUM）。