A Semantic Framework for the Security Analysis of Ethereum smart contracts
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Abstract (arXiv)
Smart contracts are programs running on cryptocurrency (e.g., Ethereum) blockchains, whose popularity stem from the possibility to perform financial transactions, such as payments and auctions, in a distributed environment without need for any trusted third party. Given their financial nature, bugs or vulnerabilities in these programs may lead to catastrophic consequences, as witnessed by recent attacks. Unfortunately, programming smart contracts is a delicate task that requires strong expertise: Ethereum smart contracts are written in Solidity, a dedicated language resembling JavaScript, and shipped over the blockchain in the EVM bytecode format. In order to rigorously verify the security of smart contracts, it is of paramount importance to formalize their semantics as well as the security properties of interest, in particular at the level of the bytecode being executed. In this paper, we present the first complete small-step semantics of EVM bytecode, which we formalize in the F* proof assistant, obtaining executable code that we successfully validate against the official Ethereum test suite. Furthermore, we formally define for the first time a number of central security properties for smart contracts, such as call integrity, atomicity, and independence from miner controlled parameters. This formalization relies on a combination of hyper- and safety properties. Along this work, we identified various mistakes and imprecisions in existing semantics and verification tools for Ethereum smart contracts, thereby demonstrating once more the importance of rigorous semantic foundations for the design of security verification techniques.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Feb 2018.
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Citing papers per year: 2021: 6; 2022: 4; 2023: 2; 2024: 1; 2025: 1; 2026: 2.
16 papers.
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- 2021Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detectioncited by 34
- 2021Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusioncited by 24
- 2021Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contractscited by 7
- 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contractscited by 2
- 2025CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchaincited by 2
- 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
4 more
- 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGramcited by 0
- 2024A Survey on Blockchain-based Supply Chain Finance with Progress and Future directionscited by 0
- 2026Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contractscited by 0
- 2026Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutionscited by 0
- 2026Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions11 May 2026
- 2026Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contracts5 May 2026
- 2025CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchain28 Apr 2025
- 2024A Survey on Blockchain-based Supply Chain Finance with Progress and Future directions14 Aug 2024
- 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts21 Apr 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
4 more
- 2021SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis3 Aug 2021
- 2021Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection24 Jul 2021
- 2021Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion17 Jun 2021
- 2021Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts7 Jan 2021
- 2021Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detectioncited by 34
- 2021Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusioncited by 24
- 2021Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contractscited by 7
- 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contractscited by 2
- 2025CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchaincited by 2
- 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
12 most cited of 16 All 16 (JSON)
- 2026Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions11 May 2026
- 2026Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contracts5 May 2026
- 2025CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchain28 Apr 2025
- 2024A Survey on Blockchain-based Supply Chain Finance with Progress and Future directions14 Aug 2024
- 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts21 Apr 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
12 newest of 16 All 16 (JSON)