DeepTx: Real-Time Transaction Risk Analysis via Multi-Modal Features and LLM Reasoning

Yixuan Liu, Xinlei Li, and Yi Li

In Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2025

Abstract: 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).

Cite:

@inproceedings{Liu2025DRT,
  author = {Liu, Yixuan and Li, Xinlei and Li, Yi},
  booktitle = {Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE)},
  month = nov,
  title = {{DeepTx}: Real-Time Transaction Risk Analysis via Multi-Modal Features and {LLM} Reasoning},
  year = {2025}
}