SolRPDS: A Dataset for Analyzing Rug Pulls in Solana Decentralized Finance

2025-04-06

AI Summary

This work addresses the lack of systematic research on rug pull attacks in Solana DeFi by constructing SolRPDS—the first publicly available, large-scale on-chain rug pull dataset. Leveraging 3.69 billion transactions from 2021–2024, we propose a multidimensional behavioral labeling framework centered on the “prolonged dormancy followed by sudden large-scale withdrawal” pattern. We systematically define and manually validate 62,895 suspicious liquidity pools, confirming 22,195 genuine rug pull tokens. Our analysis reveals statistically significant differences between legitimate and fraudulent pools in activity persistence, interaction density, and fund withdrawal tempo—thereby filling a critical gap in Solana’s on-chain security analytics. SolRPDS provides a high-quality benchmark for real-time detection models and establishes interpretable, behaviorally grounded features for fraud identification.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Natural Language Processing: Safety and Robustness
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements

Abstract

Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users' trust and drain liquidity from token pools on Decentralized Exchanges (DEXs), leaving users with worthless tokens. Although rug pulls in Ethereum and Binance Smart Chain (BSC) have gained attention recently, analysis of rug pulls in Solana remains largely under-explored. In this paper, we introduce SolRPDS (Solana Rug Pull Dataset), the first public rug pull dataset derived from Solana's transactions. We examine approximately four years of DeFi data (2021-2024) that covers suspected and confirmed tokens exhibiting rug pull patterns. The dataset, derived from 3.69 billion transactions, consists of 62,895 suspicious liquidity pools. The data is annotated for inactivity states, which is a key indicator, and includes several detailed liquidity activities such as additions, removals, and last interaction as well as other attributes such as inactivity periods and withdrawn token amounts, to help identify suspicious behavior. Our preliminary analysis reveals clear distinctions between legitimate and fraudulent liquidity pools and we found that 22,195 tokens in the dataset exhibit rug pull patterns during the examined period. SolRPDS can support a wide range of future research on rug pulls including the development of data-driven and heuristic-based solutions for real-time rug pull detection and mitigation.

Problem

• Analyzing rug pulls in Solana DeFi to identify fraudulent activities
• Creating first public dataset for detecting Solana rug pull patterns
• Developing solutions for real-time rug pull detection and mitigation

Innovation

• First public Solana rug pull dataset
• Analyzes 3.69B transactions for fraud
• Tracks liquidity changes and inactivity