SoK: Comprehensive Analysis of Rug Pull Causes, Datasets, and Detection Tools in DeFi
Dianxiang Sun, Wei Ma, Liming Nie, Yang Liu

TL;DR
This paper provides a comprehensive analysis of rug pull causes in DeFi, introduces a new taxonomy with six industry-inspired root causes, evaluates existing datasets and detection tools, and creates a more complete dataset to improve rug pull detection.
Contribution
It develops a new taxonomy of 34 rug pull root causes, expands dataset coverage, and assesses detection tools, highlighting gaps between research and real-world rug pull incidents.
Findings
Existing datasets cover only 20% of root causes.
New dataset increases coverage to 54%.
Detection tools identify 73.5% of root causes.
Abstract
Rug pulls pose a grave threat to the cryptocurrency ecosystem, leading to substantial financial loss and undermining trust in decentralized finance (DeFi) projects. With the emergence of new rug pull patterns, research on rug pull is out of state. To fill this gap, we first conducted an extensive analysis of the literature review, encompassing both scholarly and industry sources. By examining existing academic articles and industrial discussions on rug pull projects, we present a taxonomy inclusive of 34 root causes, introducing six new categories inspired by industry sources: burn, hidden owner, ownership transfer, unverified contract, external call, and fake LP lock. Based on the developed taxonomy, we evaluated current rug pull datasets and explored the effectiveness and limitations of existing detection mechanisms. Our evaluation indicates that the existing datasets, which document…
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Taxonomy
TopicsNuclear Materials and Properties · VLSI and Analog Circuit Testing · Fault Detection and Control Systems
