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They typically require participants to generate a proof-of-work before proposing a block at a given index of the chain. To choose one block among the candidate blocks at the same index, Nakamoto\u2019s consensus,\n            <jats:italic>Ghost<\/jats:italic>\n            , and the original Ethereum\u2019s consensus select, respectively, the longest branch, the heaviest subtree and the branch with the most difficult crypto-puzzles. This allows an attacker who can generate proofs-of-work faster than others to double spend by overwriting any given branch. In this article, we present a double spending attack, called the Balance attack, that simply needs to delay some messages. This result sheds new lights on an important, often implicit, assumption of the blockchain,\n            <jats:italic>synchrony<\/jats:italic>\n            , under which the transmission delay of any message should be within a known upper bound. We show that the attack succeeds with high probability on the protocols of the two largest blockchain systems in market capitalization, Bitcoin and Ethereum. To quantify the impact of our attack, we replicated the blockchain network run by 50 financial institutions and achieved double spending in less than 20 minutes. Finally, we demonstrate the success of the attack empirically by modifying the\n            <jats:italic>geth<\/jats:italic>\n            software and hijacking BGP in a controlled distributed system whose distribution of mining power is set to the distribution observed on the Ethereum main blockchain.\n          <\/jats:p>","DOI":"10.1145\/3672403","type":"journal-article","created":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T17:48:53Z","timestamp":1718300933000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Blockchain Double Spending with Low Mining Power and Network Delays"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1267-9474","authenticated-orcid":false,"given":"Chris","family":"Natoli","sequence":"first","affiliation":[{"name":"The University of Sydney, Sydney, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5050-8943","authenticated-orcid":false,"given":"Parinya","family":"Ekparinya","sequence":"additional","affiliation":[{"name":"The University of Sydney, Sydney, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4557-7690","authenticated-orcid":false,"given":"Guillaume","family":"Jourjon","sequence":"additional","affiliation":[{"name":"CSIRO, Sydney, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5632-8572","authenticated-orcid":false,"given":"Vincent","family":"Gramoli","sequence":"additional","affiliation":[{"name":"EPFL, Lausanne, Switzerland, The University of Sydney, Sydney, Australia, and Redbelly Network, Sydney, Australia"}]}],"member":"320","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"CAIDA: Center for applied internet data analysis https:\/\/www.caida.org\/ 2022."},{"key":"e_1_3_2_3_2","unstructured":"DB-IP\u2014 IP geolocation and network intelligence. 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