TL;DR
This paper introduces LLM-BSCVM, a comprehensive framework leveraging large language models and multi-agent collaboration for end-to-end smart contract vulnerability management, including detection, analysis, repair, and evaluation, enhancing security in blockchain systems.
Contribution
The paper presents a novel LLM-based framework with a three-stage Decompose-Retrieve-Generate approach for systematic vulnerability management in smart contracts, integrating multiple intelligent agents.
Findings
Achieves over 91% accuracy and F1 score in vulnerability detection.
Reduces false positive rate from 7.2% to 5.1%.
Supports continuous security monitoring with dynamic knowledge base updates.
Abstract
Smart contracts are a key component of the Web 3.0 ecosystem, widely applied in blockchain services and decentralized applications. However, the automated execution feature of smart contracts makes them vulnerable to potential attacks due to inherent flaws, which can lead to severe security risks and financial losses, even threatening the integrity of the entire decentralized finance system. Currently, research on smart contract vulnerabilities has evolved from traditional program analysis methods to deep learning techniques, with the gradual introduction of Large Language Models. However, existing studies mainly focus on vulnerability detection, lacking systematic cause analysis and Vulnerability Repair. To address this gap, we propose LLM-BSCVM, a Large Language Model-based smart contract vulnerability management framework, designed to provide end-to-end vulnerability detection,…
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