# ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection

arXiv: https://arxiv.org/abs/1807.03932 · HTML: https://mlsanity.com/paper/1807.03932/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

## Built on by (most cited first, 30 of 32)

- [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)
- [PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation](https://mlsanity.com/paper/2405.02580/) (2024)
- [Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2107.11598/) (2021)
- [Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion](https://mlsanity.com/paper/2106.09282/) (2021)
- [Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding](https://mlsanity.com/paper/2309.09826/) (2023)
- [AI Agent Smart Contract Exploit Generation](https://mlsanity.com/paper/2507.05558/) (2025)
- [ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning](https://mlsanity.com/paper/2103.12607/) (2021)
- [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)
- [HyMo: Vulnerability Detection in Smart Contracts using a Novel Multi-Modal Hybrid Model](https://mlsanity.com/paper/2304.13103/) (2023)
- [SymGPT: Auditing Smart Contracts via Combining Symbolic Execution with Large Language Models](https://mlsanity.com/paper/2502.07644/) (2025)
- [Towards Secure Program Partitioning for Smart Contracts with LLM's In-Context Learning](https://mlsanity.com/paper/2502.14215/) (2025)
- [MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge Translation](https://mlsanity.com/paper/2502.16955/) (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)
- [SourceP: Detecting Ponzi Schemes on Ethereum with Source Code](https://mlsanity.com/paper/2306.01665/) (2023)
- [Securing Smart Contract Languages with a Unified Agentic Framework for Vulnerability Repair in Solidity and Move](https://mlsanity.com/paper/2502.18515/) (2025)
- [Zer0n: An AI-Assisted Vulnerability Discovery and Blockchain-Backed Integrity Framework](https://mlsanity.com/paper/2601.07019/) (2026)
- [An Effective and Cost-Efficient Agentic Framework for Ethereum Smart Contract Auditing](https://mlsanity.com/paper/2601.17833/) (2026)
- [Graph Neural Networks Enhanced Smart Contract Vulnerability Detection of Educational Blockchain](https://mlsanity.com/paper/2303.04477/) (2023)
- [Schooling to Exploit Foolish Contracts](https://mlsanity.com/paper/2304.10737/) (2023)
- [Dual-view Aware Smart Contract Vulnerability Detection for Ethereum](https://mlsanity.com/paper/2407.00336/) (2024)
- [A Survey on Blockchain-based Supply Chain Finance with Progress and Future directions](https://mlsanity.com/paper/2408.08915/) (2024)
- [JANUS: A Difference-Oriented Analyzer For Financial Centralization Risks in Smart Contracts](https://mlsanity.com/paper/2412.03938/) (2024)
- [SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts](https://mlsanity.com/paper/2509.05681/) (2025)
- [Multi-Agent Collaborative Fuzzing with Continuous Reflection for Smart Contracts Vulnerability Detection](https://mlsanity.com/paper/2511.12164/) (2025)
- [Tailored Prompts, Targeted Protection: Vulnerability-Specific LLM Analysis for Smart Contracts](https://mlsanity.com/paper/2605.03697/) (2026)

## Abstract (arXiv)

Decentralized cryptocurrencies feature the use of blockchain to transfer values among peers on networks without central agency. Smart contracts are programs running on top of the blockchain consensus protocol to enable people make agreements while minimizing trusts. Millions of smart contracts have been deployed in various decentralized applications. The security vulnerabilities within those smart contracts pose significant threats to their applications. Indeed, many critical security vulnerabilities within smart contracts on Ethereum platform have caused huge financial losses to their users. In this work, we present ContractFuzzer, a novel fuzzer to test Ethereum smart contracts for security vulnerabilities. ContractFuzzer generates fuzzing inputs based on the ABI specifications of smart contracts, defines test oracles to detect security vulnerabilities, instruments the EVM to log smart contracts runtime behaviors, and analyzes these logs to report security vulnerabilities. Our fuzzing of 6991 smart contracts has flagged more than 459 vulnerabilities with high precision. In particular, our fuzzing tool successfully detects the vulnerability of the DAO contract that leads to USD 60 million loss and the vulnerabilities of Parity Wallet that have led to the loss of 30 million and the freezing of USD 150 million worth of Ether.