# LLM-SmartAudit: Advanced Smart Contract Vulnerability Detection

arXiv: https://arxiv.org/abs/2410.09381 · HTML: https://mlsanity.com/paper/2410.09381/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2024.

## Built on by (most cited first, 10 of 10)

- [VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection](https://mlsanity.com/paper/2509.11523/) (2025)
- [MAVUL: Multi-Agent Vulnerability Detection via Contextual Reasoning and Interactive Refinement](https://mlsanity.com/paper/2510.00317/) (2025)
- [SoK: Security and Privacy of AI Agents for Blockchain](https://mlsanity.com/paper/2509.07131/) (2025)
- [Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale](https://mlsanity.com/paper/2511.08475/) (2025)
- [Blockchain Meets LLMs: A Living Survey on Bidirectional Integration](https://mlsanity.com/paper/2411.16809/) (2024)
- [Blockchain Data Analysis in the Era of Large-Language Models](https://mlsanity.com/paper/2412.09640/) (2024)
- [Large Language Model based Smart Contract Auditing with LLMBugScanner](https://mlsanity.com/paper/2512.02069/) (2025)
- [An Effective and Cost-Efficient Agentic Framework for Ethereum Smart Contract Auditing](https://mlsanity.com/paper/2601.17833/) (2026)
- [Validating Solidity Code Defects using Symbolic and Concrete Execution powered by Large Language Models](https://mlsanity.com/paper/2509.13023/) (2025)
- [Decoupled Smart Contract Audits: Lightweight LLM Framework via Distillation and Aggregation](https://mlsanity.com/paper/2606.03128/) (2026)

## Abstract (arXiv)

The immutable nature of blockchain technology, while revolutionary, introduces significant security challenges, particularly in smart contracts. These security issues can lead to substantial financial losses. Current tools and approaches often focus on specific types of vulnerabilities. However, a comprehensive tool capable of detecting a wide range of vulnerabilities with high accuracy is lacking. This paper introduces LLM-SmartAudit, a novel framework leveraging the advanced capabilities of Large Language Models (LLMs) to detect and analyze vulnerabilities in smart contracts. Using a multi-agent conversational approach, LLM-SmartAudit employs a collaborative system with specialized agents to enhance the audit process. To evaluate the effectiveness of LLM-SmartAudit, we compiled two distinct datasets: a labeled dataset for benchmarking against traditional tools and a real-world dataset for assessing practical applications. Experimental results indicate that our solution outperforms all traditional smart contract auditing tools, offering higher accuracy and greater efficiency. Furthermore, our framework can detect complex logic vulnerabilities that traditional tools have previously overlooked. Our findings demonstrate that leveraging LLM agents provides a highly effective method for automated smart contract auditing.