eThor: Practical and Provably Sound Static Analysis of Ethereum Smart Contracts
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Abstract (arXiv)
Ethereum has emerged as the most popular smart contract development platform, with hundreds of thousands of contracts stored on the blockchain and covering a variety of application scenarios, such as auctions, trading platforms, and so on. Given their financial nature, security vulnerabilities may lead to catastrophic consequences and, even worse, they can be hardly fixed as data stored on the blockchain, including the smart contract code itself, are immutable. An automated security analysis of these contracts is thus of utmost interest, but at the same time technically challenging for a variety of reasons, such as the specific transaction-oriented programming mechanisms, which feature a subtle semantics, and the fact that the blockchain data which the contract under analysis interacts with, including the code of callers and callees, are not statically known. In this work, we present eThor, the first sound and automated static analyzer for EVM bytecode, which is based on an abstraction of the EVM bytecode semantics based on Horn clauses. In particular, our static analysis supports reachability properties, which we show to be sufficient for capturing interesting security properties for smart contracts (e.g., single-entrancy) as well as contract-specific functional properties. Our analysis is proven sound against a complete semantics of EVM bytecode and an experimental large-scale evaluation on real-world contracts demonstrates that eThor is practical and outperforms the state-of-the-art static analyzers: specifically, eThor is the only one to provide soundness guarantees, terminates on 95% of a representative set of real-world contracts, and achieves an F-measure (which combines sensitivity and specificity) of 89%.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 4; 2025: 1; 2026: 1.
11 papers.
- 2023Blockchain Large Language Modelscited by 16
- 2025SCALM: Detecting Bad Practices in Smart Contracts Through LLMscited by 8
- 2021Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contractscited by 7
- 2021ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learningcited by 4
- 2022MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilitiescited by 4
- 2024SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Frameworkcited by 3
- 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contractscited by 2
- 2024Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysiscited by 2
- 2024Dual-view Aware Smart Contract Vulnerability Detection for Ethereumcited by 0
- 2024JANUS: A Difference-Oriented Analyzer For Financial Centralization Risks in Smart Contractscited by 0
- 2026Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Trainingcited by 0
- 2026Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training29 Mar 2026
- 2025SCALM: Detecting Bad Practices in Smart Contracts Through LLMs4 Feb 2025
- 2024JANUS: A Difference-Oriented Analyzer For Financial Centralization Risks in Smart Contracts5 Dec 2024
- 2024SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Framework28 Nov 2024
- 2024Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis26 Jul 2024
- 2024Dual-view Aware Smart Contract Vulnerability Detection for Ethereum29 Jun 2024
- 2023Blockchain Large Language Models25 Apr 2023
- 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts21 Apr 2023
- 2022MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities28 Aug 2022
- 2021ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning23 Mar 2021
- 2021Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts7 Jan 2021