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The Audit Gap in Blockchain Security: A Four-Year Empirical Study of Public Audit Findings and Real-World Exploit Incidents

arXiv Security Archived Jun 16, 2026 ✓ Full text saved

arXiv:2606.15465v1 Announce Type: new Abstract: This paper presents an empirical analysis of the Web3 security landscape over the four-year and three-month period from 1 January 2022 to 27 March 2026. The dataset combines 23,818 public audit findings produced by 22 independent security firms with 218 real-world exploit incidents documented by rekt.news, representing aggregate losses of approximately US$7.76 billion. We report three central findings. First, the distribution of audit findings (by

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    Computer Science > Cryptography and Security [Submitted on 13 Jun 2026] The Audit Gap in Blockchain Security: A Four-Year Empirical Study of Public Audit Findings and Real-World Exploit Incidents Stefan Beyer This paper presents an empirical analysis of the Web3 security landscape over the four-year and three-month period from 1 January 2022 to 27 March 2026. The dataset combines 23,818 public audit findings produced by 22 independent security firms with 218 real-world exploit incidents documented by this http URL, representing aggregate losses of approximately US$7.76 billion. We report three central findings. First, the distribution of audit findings (by severity, category, and technology stack) is substantially stable across the observation window, with the Critical-plus-High share remaining within a 15-17% band in every complete year. Second, the categorical distribution of realised exploit losses does not correspond to the categorical distribution of audit findings: private-key compromise, phishing, and social-engineering vectors account for approximately 49.6% of cumulative losses yet represent a negligible share of published audit findings. Third, realised losses exhibit extreme concentration: the eight largest incidents account for 50.6% of cumulative dollar losses and the twenty largest for 71.4%, a distributional shape inconsistent with Gaussian assumptions. Throughout, we adopt the analytical convention that audit outputs and exploit outputs describe different populations and present the two datasets in parallel rather than as directly comparable samples. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.15465 [cs.CR]   (or arXiv:2606.15465v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.15465 Focus to learn more Submission history From: Stefan Beyer [view email] [v1] Sat, 13 Jun 2026 20:50:42 UTC (69 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 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
    Jun 16, 2026
    Archived
    Jun 16, 2026
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