# Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning

arXiv: https://arxiv.org/abs/2608.20271 · HTML: https://mlsanity.com/paper/2608.20271/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [The ICO Phenomenon and Its Relationships with Ethereum Smart Contract Environment](https://mlsanity.com/paper/1803.01394/) (2018)
- [Crypto Pump and Dump Detection via Deep Learning Techniques](https://mlsanity.com/paper/2205.04646/) (2022)
- [Detecting Rug Pulls in Decentralized Exchanges: Machine Learning Evidence from the TON Blockchain](https://mlsanity.com/paper/2509.01168/) (2025)
- [Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling](https://mlsanity.com/paper/2603.13830/) (2026)

## Abstract (arXiv)

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.