Trade or Trick?: Detecting and Characterizing Scam Tokens on Uniswap Decentralized Exchange

doi:10.1145/3489048.3522636

Trade or Trick?: Detecting and Characterizing Scam Tokens on Uniswap Decentralized Exchange

Published Jun 6, 2022 · Pengcheng Xia, Haoyu Wang, Bingyu Gao

Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems

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Abstract

The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Uniswap, as the most prominent cryptocurrency decentralized exchange (DEX), is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first investigate the landscape of cryptocurrency trading on Uniswap from different perspectives based on its transactions. Then, we propose an accurate approach for flagging scam tokens on Uniswap. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem.

Study Snapshot

Key takeawayApproximately 50% of tokens on Uniswap are scam tokens, resulting in at least $16 million in profit for scammers.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Trade or Trick?: Detecting and Characterizing Scam Tokens on Uniswap Decentralized Exchange

References

Trade or Trick?

50% of tokens on Uniswap are scam tokens, resulting in at least $16 million in losses for 39,762 potential victims.

Citations

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