VeriSmart: A Highly Precise Safety Verifier for Ethereum Smart Contracts
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Abstract (arXiv)
We present VeriSmart, a highly precise verifier for ensuring arithmetic safety of Ethereum smart contracts. Writing safe smart contracts without unintended behavior is critically important because smart contracts are immutable and even a single flaw can cause huge financial damage. In particular, ensuring that arithmetic operations are safe is one of the most important and common security concerns of Ethereum smart contracts nowadays. In response, several safety analyzers have been proposed over the past few years, but state-of-the-art is still unsatisfactory; no existing tools achieve high precision and recall at the same time, inherently limited to producing annoying false alarms or missing critical bugs. By contrast, VeriSmart aims for an uncompromising analyzer that performs exhaustive verification without compromising precision or scalability, thereby greatly reducing the burden of manually checking undiscovered or incorrectly-reported issues. To achieve this goal, we present a new domain-specific algorithm for verifying smart contracts, which is able to automatically discover and leverage transaction invariants that are essential for precisely analyzing smart contracts. Evaluation with real-world smart contracts shows that VeriSmart can detect all arithmetic bugs with a negligible number of false alarms, far outperforming existing analyzers.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Aug 2019.
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Citing papers per year: 2021: 3; 2022: 4; 2023: 2; 2024: 2.
11 papers.
- 2023Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectivescited by 48
- 2024Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutionscited by 5
- 2024Detecting Buggy Contracts via Smart Testingcited by 3
- 2021SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysiscited by 0
- 2021VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Searchcited by 0
- 2021Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generationcited by 0
- 2022Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectorscited by 0
- 2022Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networkscited by 0
- 2022Explanation-Guided Fairness Testing through Genetic Algorithmcited by 0
- 2022Using Graph Neural Networks for Program Terminationcited by 0
- 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGramcited by 0
- 2024Detecting Buggy Contracts via Smart Testing6 Sep 2024
- 2024Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions1 Mar 2024
- 2023Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives2 Oct 2023
- 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGram16 Mar 2023
- 2022Using Graph Neural Networks for Program Termination28 Jul 2022
- 2022Explanation-Guided Fairness Testing through Genetic Algorithm16 May 2022
- 2022Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks8 Mar 2022
- 2022Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors6 Feb 2022
- 2021Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation13 Sep 2021
- 2021VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search5 Aug 2021
- 2021SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis3 Aug 2021