A Taxonomy of Anti-Fraud Measures within Token Economy: Insights from Rug Pull Schemes

Abstract

Decentralized Finance is still a growing sector that locks in billions of USD and serves as a platform for scammers to defraud investors. The most common fraud is the so-called rug pull, which has been researched for years. This paper presents the proposed approaches to combat this scheme and proposes a taxonomy to categorize solutions according to their best application. Solutions can be applied at three different levels. The first is at the service level, where the data is freely available on the blockchain. The second is at the user level, by deploying smart contracts that consist of preventive measures; and lastly, at the verifier level, which can prevent malicious transactions in the same way they prevent double-spending. This paper contributes in three ways. It gives an overview of all published approaches to predict and prevent malicious transactions. It also presents a taxonomy to facilitate development for specific use cases. Finally, it reveals a gap in the current research landscape.