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Smart Contract Invariants Protect Against Cybercriminals

arXiv Security Archived Aug 14, 2026 ✓ Full text saved

arXiv:2608.13191v1 Announce Type: new Abstract: Blockchains are among the most adversarial environments in computing. Billions are stolen by cybercriminals who exploit vulnerabilities. This is an open problem and no concept or technique has proven to really make a difference. In this paper, we claim that the classical notion of program invariant is perhaps the most powerful solution to the problem. We devise anoriginal experimental protocol to 1) study how invariants would have protected against

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    Computer Science > Cryptography and Security [Submitted on 13 Aug 2026] Smart Contract Invariants Protect Against Cybercriminals Sofia Bobadilla, Humaira Afrin, Angela Novelli, Martin Monperrus Blockchains are among the most adversarial environments in computing. Billions are stolen by cybercriminals who exploit vulnerabilities. This is an open problem and no concept or technique has proven to really make a difference. In this paper, we claim that the classical notion of program invariant is perhaps the most powerful solution to the problem. We devise anoriginal experimental protocol to 1) study how invariants would have protected against past real-world attacks and 2) whether state-of-the-art automated tools can find them. The experimental toolchain is sophisticated. It is based on INVARIANTEVAL, a benchmark of 28 real Ethereum exploits, each paired with a human-authored invariant that blocks the attack. We validate every invariant with PONDEREPLAY, a replay framework that re-executes transactions in order to prove the correctness and soundness of smart contract invariants. We demonstrate that smart contract invariants block all the cybercriminal attacks in INVARIANTEVAL, fully validated by replaying 108,637 historical transactions. Our large-scale experiments clearly demonstrate that smart contract invariants protect against cybercriminals. Subjects: Cryptography and Security (cs.CR); Software Engineering (cs.SE) Cite as: arXiv:2608.13191 [cs.CR]   (or arXiv:2608.13191v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.13191 Focus to learn more Submission history From: Sofia Bobadilla [view email] [v1] Thu, 13 Aug 2026 12:58:36 UTC (102 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.SE 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
    Aug 14, 2026
    Archived
    Aug 14, 2026
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