Cryptology ePrint Archive: Report 2019/775

Pay To Win: Cheap, Crowdfundable, Cross-chain Algorithmic Incentive Manipulation Attacks on PoW Cryptocurrencies

Aljosha Judmayer and Nicholas Stifter and Alexei Zamyatin and Itay Tsabary and Ittay Eyal and Peter Gazi and Sarah Meiklejohn and Edgar Weippl

Abstract: In this paper we extend the attack landscape of bribing attacks on cryptocurrencies by presenting a new method, which we call Pay-To-Win (P2W). To the best of our knowledge, it is the first approach capable of facilitating double-spend collusion across different blockchains. Moreover, our technique can also be used to specifically incentivize transaction exclusion or (re)ordering. For our construction we rely on smart contracts to render the payment and receipt of bribes trustless for the briber as well as the bribee. Attacks using our approach are operated and financed out-of-band i.e., on a funding cryptocurrency, while the consequences are induced in a different target cryptocurrency. Hereby, the main requirement is that smart contracts on the funding cryptocurrency are able to verify consensus rules of the target. For a concrete instantiation of our P2W method, we choose Bitcoin as a target and Ethereum as a funding cryptocurrency. Our P2W method is designed in a way that reimburses collaborators even in the case of an unsuccessful attack. Interestingly, this actually renders our approach approximately one order of magnitude cheaper than comparable bribing techniques (e.g., the whale attack). We demonstrate the technical feasibility of P2W attacks through publishing all relevant artifacts of this paper, ranging from calculations of success probabilities to a fully functional proof-of-concept implementation, consisting of an Ethereum smart contract and a Python client.

Category / Keywords: applications / cryptocurrencies, incentives, algorithmic incentive manipulation, bribing, game theory, mechanism design, smart contracts, Bitcoin, Ethereum

Original Publication (with major differences): FC WTSC 2021

Date: received 3 Jul 2019, last revised 1 Mar 2021

Contact author: ajudmayer at sba-research org

Available format(s): PDF | BibTeX Citation

Note: All artefacts from this paper are published here: The extensive related work of this paper has been moved and extended into a Systematization of Knowledge paper (SoK) available here:

Version: 20210301:123501 (All versions of this report)

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