Detecting Scam Tokens and Rug Pull Activity on Uniswap
Summary
The study examines fraudulent tokens on Uniswap V2 using transaction data to characterize trading activity and identify suspected scams. Its detection approach combines a guilt-by-association heuristic with machine-learning techniques, aiming to flag tokens, pools, and linked addresses that may be involved in rug pulls. It also reports that some scam contracts contain mechanisms intended to deceive or restrict users.
The authors identify more than ten thousand suspected scam tokens and estimate that about half of listed tokens in their dataset were scams. They also report colluding addresses, potential victims, and estimated scam profits. These findings describe the study’s analyzed Uniswap V2 activity and its classification method; they are not universal estimates for all decentralized exchanges or later periods. The document presents early detection as a potential use of the approach but provides no detail here on model evaluation or false positive rates.
Key ideas
- The study analyzes Uniswap V2 transaction data to characterize suspected scam tokens and pools.
- Its detection method combines a guilt-by-association heuristic with machine learning.
- The authors report that many identified scams use rug pulls and that some contracts contain deceptive mechanisms.
- The analysis describes colluding addresses and estimates victims and proceeds within its dataset.
- Reported prevalence and profits depend on the study’s data and classification approach.
Tags
Full text
# Trade or Trick? Detecting and Characterizing Scam Tokens on Uniswap Decentralized Exchange # Trade or Trick? Detecting and Characterizing Scam Tokens on Uniswap Decentralized Exchange The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX. Uniswap, as the most prominent cryptocurrency 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 collect all the transactions related to Uniswap V2 exchange and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. 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 and liquidity pools 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 in league with the scam token/pool creators. 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, and our approach can act as a whistleblower that identifies scam tokens at their early stages.
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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.