# Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts

arXiv: https://arxiv.org/abs/1907.03890 · HTML: https://mlsanity.com/paper/1907.03890/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 30 of 37)

- [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)
- [PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation](https://mlsanity.com/paper/2405.02580/) (2024)
- [Blockchain Large Language Models](https://mlsanity.com/paper/2304.12749/) (2023)
- [Smart-LLaMA: Two-Stage Post-Training of Large Language Models for Smart Contract Vulnerability Detection and Explanation](https://mlsanity.com/paper/2411.06221/) (2024)
- [Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts](https://mlsanity.com/paper/2101.02377/) (2021)
- [ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning](https://mlsanity.com/paper/2103.12607/) (2021)
- [MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities](https://mlsanity.com/paper/2208.13252/) (2022)
- [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)
- [AiRacleX: Automated Detection of Price Oracle Manipulations via LLM-Driven Knowledge Mining and Prompt Generation](https://mlsanity.com/paper/2502.06348/) (2025)
- [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)
- [SymGPT: Auditing Smart Contracts via Combining Symbolic Execution with Large Language Models](https://mlsanity.com/paper/2502.07644/) (2025)
- [SAEL: Leveraging Large Language Models with Adaptive Mixture-of-Experts for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2507.22371/) (2025)
- [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)
- [Legal Compliance Evaluation of Smart Contracts Generated By Large Language Models](https://mlsanity.com/paper/2506.00943/) (2025)
- [Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges](https://mlsanity.com/paper/2508.02773/) (2025)
- [Large Language Model based Smart Contract Auditing with LLMBugScanner](https://mlsanity.com/paper/2512.02069/) (2025)
- [BugSweeper: Function-Level Detection of Smart Contract Vulnerabilities Using Graph Neural Networks](https://mlsanity.com/paper/2512.09385/) (2025)
- [PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance](https://mlsanity.com/paper/2608.17220/) (2026)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)

## Abstract (arXiv)

An effective way to maximize code coverage in software tests is through dynamic symbolic execution-a technique that uses constraint solving to systematically explore a program's state space. We introduce an open-source dynamic symbolic execution framework called Manticore for analyzing binaries and Ethereum smart contracts. Manticore's flexible architecture allows it to support both traditional and exotic execution environments, and its API allows users to customize their analysis. Here, we discuss Manticore's architecture and demonstrate the capabilities we have used to find bugs and verify the correctness of code for our commercial clients.