2026-10-02T23:07:12Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27883 2026-10-02T17:40:23Z ddc:004 open_access

When Does Being Selfish Pay Off? Temporal Composability and Profitability in Selfish Mining Finkbeiner, Colin Shaw, Connor Almashaqbeh, Ghada Bitcoin Proof of work Selfish mining Temporal mining strategies Selfish mining undermines incentive compatibility of proof-of-work blockchains, letting a miner earn disproportionate rewards at a hashrate lower than the majority threshold. A decade of research has investigated whether a strategy is profitable, however, far less is understood about when it becomes profitable. Timing is critical since selfish mining operates at a loss before it turns a profit, typically requiring tens of weeks to break even in the classic case. In this paper, we present a holistic study of the time-to-profitability (TTP) of existing selfish mining strategies structured around four contributions. First, in the single-attacker setting, we characterize TTP across the full strategy space and find that TTP-minimizing and profit-maximizing strategies frequently diverge, making attack horizon a critical metric. In particular, under realistic fee dynamics, the use of incentive transactions to recruit honest-but-rational miners enables reaching profitability up to 15× faster than classic selfish mining at the same hash rate. Second, we study TTP for the first time in the multi-attacker setting, showing that the difference in strategies between opposing attackers has a dramatic impact on join profitability. Third, we generalize intermittent selfish mining by examining temporal composition over the full strategy space and showing that its purported benefits are largely overstated; alternating strategies rarely outperforms the best static strategy in terms of TTP and long-term profits. Finally, and building on these findings, we investigate adaptive, state-conditioned strategy selection at the difficulty adjustment period (DAP) level. We compare a general-purpose LLM agent against a fixed decision-tree selector. We find that both selectors reliably identify profit-maximizing strategies from observed network conditions, at a low operational cost, thus lowering the expertise barrier to exploiting adaptive selfish mining. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Colin Finkbeiner and Connor Shaw and Ghada Almashaqbeh 2026 Is Part Of LIPIcs, Volume 395, 8th Conference on Advances in Financial Technologies (AFT 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.AFT.2026.29 urn:nbn:de:0030-drops-278839 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2026.29 eng https://creativecommons.org/licenses/by/4.0/legalcode