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Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB)

Federico Cernera, Massimo La Morgia, Alessandro Mei, Francesco Sassi

TL;DR

The paper tackles the problem of unstable token ecosystems and fraudulent on-chain trading in Ethereum and the BNB Smart Chain by constructing large-scale Token and Liquidity Pool datasets and performing a longitudinal analysis up to March 2022. The authors introduce and quantify phenomena such as 1-day rug pulls and token spammers, estimate profits on the order of $150$–$240$ million across chains, and reveal extensive Sniper Bot 2.0 activity that accelerates listing-time purchases. Their contributions include detailed lifecycle metrics for tokens, cross-chain comparisons of rug-pull mechanics, and practical mitigation ideas (lifetime-based risk, liquidity-distribution signals, and deceptive-name detection) to inform defense by AMMs and regulators. The work highlights the vulnerability of cost-efficient blockchains to rapid, low-effort exploits and underscores the need for robust detection and anti-fraud mechanisms in DeFi. These insights have significant implications for users, developers, and policymakers aiming to improve DeFi safety and market integrity.

Abstract

In this work, we perform a longitudinal analysis of the BNB Smart Chain and Ethereum blockchain from their inception to March 2022. We study the ecosystem of the tokens and liquidity pools, highlighting analogies and differences between the two blockchains. We discover that about 60% of tokens are active for less than one day. Moreover, we find that 1% of addresses create an anomalous number of tokens (between 20% and 25%). We discover that these tokens are used as disposable tokens to perform a particular type of rug pull, which we call 1-day rug pull. We quantify the presence of this operation on both blockchains discovering its prevalence on the BNB Smart Chain. We estimate that 1-day rug pulls generated $240 million in profits. Finally, we present sniper bots, a new kind of trader bot involved in these activities, and we detect their presence and quantify their activity in the rug pull operations.

Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB)

TL;DR

The paper tackles the problem of unstable token ecosystems and fraudulent on-chain trading in Ethereum and the BNB Smart Chain by constructing large-scale Token and Liquidity Pool datasets and performing a longitudinal analysis up to March 2022. The authors introduce and quantify phenomena such as 1-day rug pulls and token spammers, estimate profits on the order of – million across chains, and reveal extensive Sniper Bot 2.0 activity that accelerates listing-time purchases. Their contributions include detailed lifecycle metrics for tokens, cross-chain comparisons of rug-pull mechanics, and practical mitigation ideas (lifetime-based risk, liquidity-distribution signals, and deceptive-name detection) to inform defense by AMMs and regulators. The work highlights the vulnerability of cost-efficient blockchains to rapid, low-effort exploits and underscores the need for robust detection and anti-fraud mechanisms in DeFi. These insights have significant implications for users, developers, and policymakers aiming to improve DeFi safety and market integrity.

Abstract

In this work, we perform a longitudinal analysis of the BNB Smart Chain and Ethereum blockchain from their inception to March 2022. We study the ecosystem of the tokens and liquidity pools, highlighting analogies and differences between the two blockchains. We discover that about 60% of tokens are active for less than one day. Moreover, we find that 1% of addresses create an anomalous number of tokens (between 20% and 25%). We discover that these tokens are used as disposable tokens to perform a particular type of rug pull, which we call 1-day rug pull. We quantify the presence of this operation on both blockchains discovering its prevalence on the BNB Smart Chain. We estimate that 1-day rug pulls generated $240 million in profits. Finally, we present sniper bots, a new kind of trader bot involved in these activities, and we detect their presence and quantify their activity in the rug pull operations.
Paper Structure (27 sections, 1 equation, 5 figures, 6 tables)

This paper contains 27 sections, 1 equation, 5 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Ethereum and BNB Smart Chain
  3. AMMs, Uniswap and its forks
  4. The Datasets
  5. The Token dataset
  6. Gathering smart contracts
  7. Token identification
  8. Liquidity Pools dataset
  9. The Lifetime of tokens
  10. Token spammers
  11. The Anatomy of a Rug Pull
  12. Looking for 1-day Rug Pulls
  13. Estimating the gains of the operations
  14. Results
  15. The gains
  16. ...and 12 more sections

Figures (5)

  • Figure 1: Lifetime of tokens and liquidity pools on BSC and Ethereum.
  • Figure 2: Distribution of the number of tokens created by the addresses that create at least one token in BSC and Ethereum. For the sake of visualization, the CDF is cut at 100 tokens.
  • Figure 3: Fraction of addresses that create at least one token with respect to the fraction of tokens that they create.
  • Figure 4: The figure shows the number of rug pull operations (a), the initial liquidity added to each pool (b), and the gain for each operation over time. All the metrics are aggregated daily. The dashed vertical lines divide the three phases we identify.
  • Figure 5: Scatter plot of the number of liquidity pools with a 1-day rug pull pattern where the address swapped and the average delay from the pool creation.