MOS introduces a novel framework that combines large language models with domain expertise to detect vulnerabilities in blockchain smart contracts with higher accuracy and fewer false positives.

• Overcomes limitations of traditional methods by using a mixture-of-experts approach to fine-tune LLMs for security analysis
• Provides explainable results unlike black-box deep learning methods
• Significantly reduces false positive rates compared to standard LLM approaches
• Demonstrates practical security improvements for blockchain systems vulnerable to financial exploitation

This research offers security teams a more reliable tool for identifying critical vulnerabilities before deployment, potentially preventing millions in financial losses from smart contract exploits.

MOS: Towards Effective Smart Contract Vulnerability Detection through Mixture-of-Experts Tuning of Large Language Models