# Vandal: A Scalable Security Analysis Framework for Smart Contracts

arXiv: https://arxiv.org/abs/1809.03981 · HTML: https://mlsanity.com/paper/1809.03981/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 18 of 18)

- [GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis](https://mlsanity.com/paper/2308.03314/) (2023)
- [Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives](https://mlsanity.com/paper/2310.01152/) (2023)
- [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024)
- [SCALM: Detecting Bad Practices in Smart Contracts Through LLMs](https://mlsanity.com/paper/2502.04347/) (2025)
- [Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts](https://mlsanity.com/paper/2101.02377/) (2021)
- [Semia: Auditing Agent Skills via Constraint-Guided Representation Synthesis](https://mlsanity.com/paper/2605.00314/) (2026)
- [Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilities](https://mlsanity.com/paper/2501.07058/) (2025)
- [ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning](https://mlsanity.com/paper/2103.12607/) (2021)
- [An Automated Vulnerability Detection Framework for Smart Contracts](https://mlsanity.com/paper/2301.08824/) (2023)
- [Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis](https://mlsanity.com/paper/2407.18639/) (2024)
- [SymGPT: Auditing Smart Contracts via Combining Symbolic Execution with Large Language Models](https://mlsanity.com/paper/2502.07644/) (2025)
- [CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchain](https://mlsanity.com/paper/2504.21043/) (2025)
- [Deep Smart Contract Intent Detection](https://mlsanity.com/paper/2211.10724/) (2022)
- [Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods](https://mlsanity.com/paper/2211.14779/) (2022)
- [MindTheDApp: A Toolchain for Complex Network-Driven Structural Analysis of Ethereum-based Decentralised Applications](https://mlsanity.com/paper/2310.02408/) (2023)
- [SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts](https://mlsanity.com/paper/2509.05681/) (2025)
- [Esim: EVM Bytecode Similarity Detection Based on Stable-Semantic Graph](https://mlsanity.com/paper/2511.12971/) (2025)
- [Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contracts](https://mlsanity.com/paper/2605.03697/) (2026)

## Abstract (arXiv)

The rise of modern blockchains has facilitated the emergence of smart contracts: autonomous programs that live and run on the blockchain. Smart contracts have seen a rapid climb to prominence, with applications predicted in law, business, commerce, and governance. Smart contracts are commonly written in a high-level language such as Ethereum's Solidity, and translated to compact low-level bytecode for deployment on the blockchain. Once deployed, the bytecode is autonomously executed, usually by a %Turing-complete virtual machine. As with all programs, smart contracts can be highly vulnerable to malicious attacks due to deficient programming methodologies, languages, and toolchains, including buggy compilers. At the same time, smart contracts are also high-value targets, often commanding large amounts of cryptocurrency. Hence, developers and auditors need security frameworks capable of analysing low-level bytecode to detect potential security vulnerabilities. In this paper, we present Vandal: a security analysis framework for Ethereum smart contracts. Vandal consists of an analysis pipeline that converts low-level Ethereum Virtual Machine (EVM) bytecode to semantic logic relations. Users of the framework can express security analyses in a declarative fashion: a security analysis is expressed in a logic specification written in the souffle language. We conduct a large-scale empirical study for a set of common smart contract security vulnerabilities, and show the effectiveness and efficiency of Vandal. Vandal is both fast and robust, successfully analysing over 95% of all 141k unique contracts with an average runtime of 4.15 seconds; outperforming the current state of the art tools---Oyente, EthIR, Mythril, and Rattle---under equivalent conditions.