Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning
2026-08-20 • Artificial Intelligence
Artificial IntelligenceDistributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors studied scams called rug pulls in memecoins on the Solana blockchain, where many tokens quickly disappear with investors' money. They found that unlike Ethereum, these scams often happen through tricks with liquidity and social manipulation. By analyzing 6.4 million tokens over 7 months, they show most scams happen within an hour of launch. Using just the first 5 minutes of trading data, their machine learning model can effectively spot potential rug pulls. They also found combining data from different sources improves scam detection across platforms.
memecoinsrug pullsSolana blockchainliquidity manipulationmachine learningXGBoostDeFi fraudtoken tradingcross-platform detectiondata fusion
Authors
Jianghai Li, Pavel Kuznetsov, Yury Yanovich, Konstantin Nott-Whaley, Igor Vodolazov
Abstract
The rapid proliferation of memecoins on blockchain platforms has increased the risk of fraudulent activities, particularly rug pulls. While previous studies have focused on Ethereum-based tokens, this paper shifts the spotlight to Solana, the leading blockchain for memecoins by trading volume and token count. Unlike Ethereum, where rug pulls often exploit smart contract backdoors, Solana memecoin rug pulls are predominantly driven by liquidity manipulation and social dynamics. This research pioneers large-scale rug pull early detection in the Solana ecosystem by assembling a dataset of 6.4 million tokens over 7 months. Market analysis reveals that a vast majority of these memecoins exhibit rug pull characteristics within one hour of launch, highlighting the urgency of short-horizon prediction. Despite the absence of code-level features, we demonstrate that classic machine learning models, particularly Gradient Boosting (XGBoost), achieve robust performance in detecting potential rug pulls using only the first 5 minutes of trading data. Furthermore, we evaluate cross-platform generalization between PumpFun and Raydium, revealing that multi-source data fusion significantly mitigates domain shift and improves detection reliability. This study advances the understanding of DeFi fraud on high-throughput chains and provides a practical framework for protecting investors.