Collaborative LLM Reasoning for Vulnerability Detection in Smart Contracts

Abstract

Smart contracts play a pivotal role in decentralized applications but are subject to security vulnerabilities often difficult to detect. Traditional static and symbolic analysis tools cannot handle intricate logic and are limited in adaptability and explainability. Recent development of large language models (LLMs) provide new opportunities for vulnerability detection, but single-model methods often suffer from inconsistency and prompt sensitivity. This paper introduces a collaborative LLM-based model that enhances detection robustness through semantic similarity-based few-shot prompting and multi-LLM reasoning. Our model integrates diverse LLMs (ChatGPT, Gemini, Grok) as worker nodes, along with an aggregator model to resolve disagreements via justification analysis and final prediction consolidation. Experimental evaluations on the SmartBugs benchmark demonstrated a remarkable enhancement in detection accuracy (96.25%) and response time compared to other models. The proposed model provides a scalable and explainable solution for smart contract auditing, illustrating the strength of LLM collaboration in security-critical applications.