CyberIntel ⬡ News
★ Saved ◆ Cyber Reads
← Back ◬ AI & Machine Learning Aug 14, 2026

Slow and Steady: Preventing MEV with Verifiable Delays

arXiv Security Archived Aug 14, 2026 ✓ Full text saved

arXiv:2608.13271v1 Announce Type: new Abstract: Our work presents a defense mechanism against Maximal Extractable Value (MEV) opportunities in distributed ledgers. The mechanism relies on the idea of enforcing a verifiable delay when generating transactions, such that a block creator cannot react to the appearance of a MEV opportunity without breaking liveness. We present positive results both in the Byzantine setting and in a game theoretic model of rational participants. We additionally presen

Full text archived locally
✦ AI Summary · Claude Sonnet


    Computer Science > Cryptography and Security [Submitted on 13 Aug 2026] Slow and Steady: Preventing MEV with Verifiable Delays Zeta Avarikioti, Dimitris Karakostas, Karl Kreder, Shreekara Shastry Our work presents a defense mechanism against Maximal Extractable Value (MEV) opportunities in distributed ledgers. The mechanism relies on the idea of enforcing a verifiable delay when generating transactions, such that a block creator cannot react to the appearance of a MEV opportunity without breaking liveness. We present positive results both in the Byzantine setting and in a game theoretic model of rational participants. We additionally present negative bounds that outline the limitations of this line of defense. Finally, we explore real-world implementation details of verifiable delays and show that, based on historical MEV data, our mechanism could realistically help prevent most existing MEV threats. Comments: A revised version is accepted for publication at the 10th International Workshop on Cryptocurrencies and Blockchain Technology (CBT 2026) Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.13271 [cs.CR]   (or arXiv:2608.13271v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.13271 Focus to learn more Submission history From: Dimitris Karakostas [view email] [v1] Thu, 13 Aug 2026 14:08:34 UTC (51 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
    💬 Team Notes
    Article Info
    Source
    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Aug 14, 2026
    Archived
    Aug 14, 2026
    Full Text
    ✓ Saved locally
    Open Original ↗