Price manipulation schemes of new crypto-tokens in decentralized exchanges

TL;DR

This work analyzes the financial dynamics of newly created tokens on Uniswap V2, emphasizing honeypots, rug pulls, and sandwich attacks. It combines a large on-chain dataset with a buy-and-hold framework and a Net Traded Value metric to quantify real liquidity and risk, and contrasts price evolution in swap time versus physical time using clustering and DTW techniques. The findings show that most new-token liquidity is trapped in honeypots, producing misleadingly high apparent profits, while sellable tokens still offer meaningful but more modest gains; sandwich attacks are a key driver of extreme but fragile profits in low-liquidity pools. Practically, the study highlights the need for caution, security assessments, and consideration of block-time dynamics in decentralized markets, with implications for investors and MEV-aware trading strategies.

Abstract

Blockchain technology has revolutionized financial markets by enabling decentralized exchanges (DEXs) that operate without intermediaries. Uniswap V2, a leading DEX, facilitates the rapid creation and trading of new tokens, which offer high return potential but exposing investors to significant risks. In this work, we analyze the financial impact of newly created tokens, assessing their market dynamics, profitability and liquidity manipulations. Our findings reveal that a significant portion of market liquidity is trapped in honeypots, reducing market efficiency and misleading investors. Applying a simple buy-and-hold strategy, we are able to uncover some major risks associated with investing in newly created tokens, including the widespread presence of rug pulls and sandwich attacks. We extract the optimal sandwich amount, revealing that their proliferation in new tokens stems from higher profitability in low-liquidity pools. Furthermore, we analyze the fundamental differences between token price evolution in swap time and physical time. Using clustering techniques, we highlight these differences and identify typical patterns of honeypot and sellable tokens. Our study provides insights into the risks and financial dynamics of decentralized markets and their challenges for investors.

Figures (18)

• Figure 1: Normalized distributions
• Figure 2: Distribution of profits in WETH generated by our strategy. Among the 7,213 tokens in which the algorithm invested, 2,101 ($\sim 29.1\%$) failed to exit their position, resulting in a fixed loss of $P_{\text{loss}} = -0.018$ WETH. All remaining tokens yielded a positive profit. To improve visualization, we restrict the distribution to the range between 0 and 0.015 WETH. Beyond this threshold, there are an additional 162 tokens reaching a maximum profit of 1,056.07 WETH.
• Figure 3: Histograms of the NTV of the new tokens after different number of swaps.
• Figure 4: Scatter plot showing the maximum NTV value in the lifetime of all new tokens against the corresponding liquidity value at that time. The $0.14\%$ of tokens with a maximum negative NTV are excluded. A negative NTV occurs when a hidden supply held by the token creator, which was not included in the initial mint, is used to sell into the pool.
• Figure 5: The figures show the mean and the one standard deviation bands of the trajectories resulting from the clustering, with $k=2$ and $k=3$ clusters in swap time.