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DeFiAligner: Leveraging Symbolic Analysis and Large Language Models for Inconsistency Detection in Decentralized Finance

Authors Rundong Gan, Liyi Zhou, Le Wang , Kaihua Qin, Xiaodong Lin

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Author Details

Rundong Gan
  • School of Computer Science, University of Guelph, Canada
Liyi Zhou
  • The University of Sydney, Australia
  • UC Berkeley RDI, CA, USA
  • Decentralized Intelligence AG, Zug, Switzerland
Le Wang
  • School of Computer Science, University of Guelph, Canada
Kaihua Qin
  • Yale University, New Haven, CT, USA
  • UC Berkeley RDI, CA, USA
  • Decentralized Intelligence AG, Zug, Switzerland
Xiaodong Lin
  • School of Computer Science, University of Guelph, Canada

Funding

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC).

Cite AsGet BibTex

Rundong Gan, Liyi Zhou, Le Wang, Kaihua Qin, and Xiaodong Lin. DeFiAligner: Leveraging Symbolic Analysis and Large Language Models for Inconsistency Detection in Decentralized Finance. In 6th Conference on Advances in Financial Technologies (AFT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 316, pp. 7:1-7:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.AFT.2024.7

Abstract

Decentralized Finance (DeFi) has witnessed a monumental surge, reaching 53.039 billion USD in total value locked. As this sector continues to expand, ensuring the reliability of DeFi smart contracts becomes increasingly crucial. While some users are adept at reading code or the compiled bytecode to understand smart contracts, many rely on documentation. Therefore, discrepancies between the documentation and the deployed code can pose significant risks, whether these discrepancies are due to errors or intentional fraud. To tackle these challenges, we developed DeFiAligner, an end-to-end system to identify inconsistencies between documentation and smart contracts. DeFiAligner incorporates a symbolic execution tool, SEVM, which explores execution paths of on-chain binary code, recording memory and stack states. It automatically generates symbolic expressions for token balance changes and branch conditions, which, along with related project documents, are processed by LLMs. Using structured prompts, the LLMs evaluate the alignment between the symbolic expressions and the documentation. Our tests across three distinct scenarios demonstrate DeFiAligner’s capability to automate inconsistency detection in DeFi, achieving recall rates of 92% and 90% on two public datasets respectively.

Subject Classification

ACM Subject Classification
  • Security and privacy → Distributed systems security
Keywords
  • Decentralized Finance Security
  • Large Language Models
  • Project Review
  • Symbolic Analysis
  • Smart Contracts

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