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Position Paper: Denial-of-Service Against Multi-Round Transaction Simulation

arXiv Security Archived Apr 24, 2026 ✓ Full text saved

arXiv:2604.21169v1 Announce Type: new Abstract: In Ethereum, transaction-bundling services are a critical component of block builders, such as Flashbots Bundles, and are widely used by MEV searchers. Disrupting bundling services can degrade searcher experience and reduce builder revenue. Despite the extensive studies, the existing denial-of-service attack designs are ineffective against bundling services due to their unique multi-round execution model. This paper studies the open problem of asym

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    Computer Science > Cryptography and Security [Submitted on 23 Apr 2026] Position Paper: Denial-of-Service Against Multi-Round Transaction Simulation Yuzhe Tang, Yibo Wang, Wanning Ding, Jiaqi Chen, Taesoo Kim In Ethereum, transaction-bundling services are a critical component of block builders, such as Flashbots Bundles, and are widely used by MEV searchers. Disrupting bundling services can degrade searcher experience and reduce builder revenue. Despite the extensive studies, the existing denial-of-service attack designs are ineffective against bundling services due to their unique multi-round execution model. This paper studies the open problem of asymmetric denial-of-service against bundling services. We develop evasive, risk-free, and low-cost DoS attacks on Flashbots' bundling service, the only open-source bundling service known to us. Our attacks exploit inter-transaction dependencies through contract state to achieve evasiveness, and abuse bundling-specific features, such as atomic block inclusion, to significantly reduce both capital and operational costs of the attack. Experimental results show that our attacks achieve high success rates, substantially reduce builders' revenue, and slow block production. We further propose mitigation strategies for the identified risks. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.21169 [cs.CR]   (or arXiv:2604.21169v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.21169 Focus to learn more Submission history From: Yuzhe Tang [view email] [v1] Thu, 23 Apr 2026 00:25:44 UTC (146 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 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?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 24, 2026
    Archived
    Apr 24, 2026
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