SAILFISH: Vetting Smart Contract State-Inconsistency Bugs in Seconds
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Abstract (arXiv)
This paper presents SAILFISH, a scalable system for automatically finding state-inconsistency bugs in smart contracts. To make the analysis tractable, we introduce a hybrid approach that includes (i) a light-weight exploration phase that dramatically reduces the number of instructions to analyze, and (ii) a precise refinement phase based on symbolic evaluation guided by our novel value-summary analysis, which generates extra constraints to over-approximate the side effects of whole-program execution, thereby ensuring the precision of the symbolic evaluation. We developed a prototype of SAILFISH and evaluated its ability to detect two state-inconsistency flaws, viz., reentrancy and transaction order dependence (TOD) in Ethereum smart contracts. Further, we present detection rules for other kinds of smart contract flaws that SAILFISH can be extended to detect. Our experiments demonstrate the efficiency of our hybrid approach as well as the benefit of the value summary analysis. In particular, we show that S SAILFISH outperforms five state-of-the-art smart contract analyzers (SECURITY, MYTHRIL, OYENTE, SEREUM and VANDAL ) in terms of performance, and precision. In total, SAILFISH discovered 47 previously unknown vulnerable smart contracts out of 89,853 smart contracts from ETHERSCAN .
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2021.
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Citing papers per year: 2023: 3; 2024: 1; 2025: 3; 2026: 3.
10 papers.
- 2023GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysiscited by 65
- 2023Blockchain Large Language Modelscited by 16
- 2025Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detectioncited by 7
- 2024Detecting Buggy Contracts via Smart Testingcited by 3
- 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contractscited by 2
- 2026Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarizationcited by 1
- 2025SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contractscited by 0
- 2025Multi-Agent Collaborative Fuzzing with Continuous Reflection for Smart Contracts Vulnerability Detectioncited by 0
- 2026AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction poolingcited by 0
- 2026Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contractscited by 0
- 2026Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contracts17 Jul 2026
- 2026AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling4 Jun 2026
- 2026Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization27 Mar 2026
- 2025Multi-Agent Collaborative Fuzzing with Continuous Reflection for Smart Contracts Vulnerability Detection15 Nov 2025
- 2025SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts6 Sep 2025
- 2025Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection23 Jun 2025
- 2024Detecting Buggy Contracts via Smart Testing6 Sep 2024
- 2023GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis7 Aug 2023
- 2023Blockchain Large Language Models25 Apr 2023
- 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts21 Apr 2023