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

• URL: http://arxiv.org/abs/2504.07132v1
• Date: Sun, 06 Apr 2025 11:36:48 GMT
• Title: SolRPDS: A Dataset for Analyzing Rug Pulls in Solana Decentralized Finance
• Authors: Abdulrahman Alhaidari, Bhavani Kalal, Balaji Palanisamy, Shamik Sural,
• Abstract summary: Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi)<n>A rug pull occurs when developers exploit users' trust and drain liquidity from token pools on Decentralized Exchanges (DEXs)<n>We introduce SolRPDS, the first public rug pull dataset derived from Solana's transactions.
• Score: 0.6367946001576646
• License: http://creativecommons.org/licenses/by/4.0/
• 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activities such as additions, removals, and last interaction as well as other attributes such as inactivity periods and withdrawn token amounts, to help identify suspicious behavior.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.

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