# SmartBugs: A Framework to Analyze Solidity Smart Contracts

arXiv: https://arxiv.org/abs/2007.04771 · HTML: https://mlsanity.com/paper/2007.04771/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

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

- [LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights](https://mlsanity.com/paper/2502.07049/) (2025)
- [Smart-LLaMA: Two-Stage Post-Training of Large Language Models for Smart Contract Vulnerability Detection and Explanation](https://mlsanity.com/paper/2411.06221/) (2024)
- [Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview](https://mlsanity.com/paper/2503.10784/) (2025)
- [Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2506.18245/) (2025)
- [Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilities](https://mlsanity.com/paper/2501.07058/) (2025)
- [MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities](https://mlsanity.com/paper/2208.13252/) (2022)
- [VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model](https://mlsanity.com/paper/2309.08474/) (2023)
- [Prompt to Pwn: Automated Exploit Generation for Smart Contracts](https://mlsanity.com/paper/2508.01371/) (2025)
- [Detecting Buggy Contracts via Smart Testing](https://mlsanity.com/paper/2409.04597/) (2024)
- [Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts](https://mlsanity.com/paper/2304.10726/) (2023)
- [Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis](https://mlsanity.com/paper/2407.18639/) (2024)
- [Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities](https://mlsanity.com/paper/2409.10574/) (2024)
- [SAEL: Leveraging Large Language Models with Adaptive Mixture-of-Experts for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2507.22371/) (2025)
- [Schooling to Exploit Foolish Contracts](https://mlsanity.com/paper/2304.10737/) (2023)
- [Towards Automated Smart Contract Generation: Evaluation, Benchmarking, and Retrieval-Augmented Repair](https://mlsanity.com/paper/2503.01098/) (2025)
- [Towards Understanding Deep Learning Model in Image Recognition via Coverage Test](https://mlsanity.com/paper/2505.08814/) (2025)
- [Towards Compositional Generalization in LLMs for Smart Contract Security: A Case Study on Reentrancy Vulnerabilities](https://mlsanity.com/paper/2601.06914/) (2026)
- [Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions](https://mlsanity.com/paper/2605.11163/) (2026)

## Abstract (arXiv)

Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present SmartBugs, an extensible and easy-to-use execution framework that simplifies the execution of analysis tools on smart contracts written in Solidity, the primary language used in Ethereum. SmartBugs is currently distributed with support for 10 tools and two datasets of Solidity contracts. The first dataset can be used to evaluate the precision of analysis tools, as it contains 143 annotated vulnerable contracts with 208 tagged vulnerabilities. The second dataset contains 47,518 unique contracts collected through Etherscan. We discuss how SmartBugs supported the largest experimental setup to date both in the number of tools and in execution time. Moreover, we show how it enables easy integration and comparison of analysis tools by presenting a new extension to the tool SmartCheck that improves substantially the detection of vulnerabilities related to the DASP10 categories Bad Randomness, Time Manipulation, and Access Control (identified vulnerabilities increased from 11% to 24%).