Forsage: Anatomy of a Smart-Contract Pyramid Scheme

Pyramid schemes are investment scams in which top-level participants in a hierarchical network recruit and profit from an expanding base of defrauded newer participants. Pyramid schemes have existed for over a century, but there have been no in-depth studies of their dynamics and communities because of the opacity of participants’ transactions. In this paper, we present an empirical study of Forsage, a pyramid scheme implemented as a smart contract and at its peak one of the largest consumers of resources in Ethereum. As a smart contract, Forsage makes its (byte)code and all of its transactions visible on the blockchain. We take advantage of this unprecedented transparency to gain insight into the mechanics, impact on participants, and evolution of Forsage. We quantify the (multi-million-dollar) gains of top-level participants as well as the losses of the vast majority (around 88%) of users. We analyze Forsage code both manually and using a purpose-built transaction simulator to uncover the complex mechanics of the scheme. Through complementary study of promotional videos and social media, we show how Forsage promoters have leveraged the unique features of smart contracts to lure users with false claims of trustworthiness and profitability, and how Forsage activity is concentrated within a small number of national communities.

[1]  Alessandro Flammini,et al.  Detection of Novel Social Bots by Ensembles of Specialized Classifiers , 2020, CIKM.

[2]  Sarah Meiklejohn,et al.  Tracing Transactions Across Cryptocurrency Ledgers , 2018, USENIX Security Symposium.

[3]  Yajin Zhou,et al.  Don’t Fish in Troubled Waters! Characterizing Coronavirus-themed Cryptocurrency Scams , 2020, 2020 APWG Symposium on Electronic Crime Research (eCrime).

[4]  Tyler Moore,et al.  There's No Free Lunch, Even Using Bitcoin: Tracking the Popularity and Profits of Virtual Currency Scams , 2015, Financial Cryptography.

[5]  Massimo Bartoletti,et al.  Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact , 2017, Future Gener. Comput. Syst..

[6]  Benjamin Livshits,et al.  The Anatomy of a Cryptocurrency Pump-and-Dump Scheme , 2018, USENIX Security Symposium.

[7]  Filippo Menczer,et al.  Online Human-Bot Interactions: Detection, Estimation, and Characterization , 2017, ICWSM.

[8]  Stefan Savage,et al.  PharmaLeaks: Understanding the Business of Online Pharmaceutical Affiliate Programs , 2012, USENIX Security Symposium.

[9]  Kristie B. Hadden,et al.  2020 , 2020, Journal of Surgical Orthopaedic Advances.

[10]  Mathis Steichen,et al.  The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts , 2019, USENIX Security Symposium.

[11]  J. Kamps,et al.  To the moon: defining and detecting cryptocurrency pump-and-dumps , 2018, Crime Science.

[12]  Shanqing Yu,et al.  Ponzi Scheme Detection in EthereumTransaction Network , 2021, BlockSys.

[13]  Neil Gandal,et al.  An Examination of the Cryptocurrency Pump and Dump Ecosystem , 2018, Inf. Process. Manag..

[14]  Malte Möser,et al.  Effective Cryptocurrency Regulation Through Blacklisting , 2019 .

[15]  Bernhard Haslhofer,et al.  Spams meet Cryptocurrencies: Sextortion in the Bitcoin Ecosystem , 2019, AFT.

[16]  Xin Chen,et al.  The DAO attack paradoxes in propositional logic , 2017, 2017 4th International Conference on Systems and Informatics (ICSAI).

[17]  Vitalik Buterin A NEXT GENERATION SMART CONTRACT & DECENTRALIZED APPLICATION PLATFORM , 2015 .

[18]  Massimo Bartoletti,et al.  Data Mining for Detecting Bitcoin Ponzi Schemes , 2018, 2018 Crypto Valley Conference on Blockchain Technology (CVCBT).

[19]  Ross C. Phillips,et al.  Tracing Cryptocurrency Scams: Clustering Replicated Advance-Fee and Phishing Websites , 2020, 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC).