Large Language Model based Smart Contract Auditing with LLMBugScanner

• URL: http://arxiv.org/abs/2512.02069v1
• Date: Sat, 29 Nov 2025 19:13:44 GMT
• Title: Large Language Model based Smart Contract Auditing with LLMBugScanner
• Authors: Yining Yuan, Yifei Wang, Yichang Xu, Zachary Yahn, Sihao Hu, Ling Liu,
• Abstract summary: Smart contract auditing presents several challenges for large language models (LLM)<n>No single model performs consistently well across all vulnerability types or contract structures.<n>LLMBugScanner combines domain knowledge adaptation with ensemble reasoning to improve robustness and generalization.
• Score: 16.70822025530469
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents LLMBugScanner, a large language model (LLM) based framework for smart contract vulnerability detection using fine-tuning and ensemble learning. Smart contract auditing presents several challenges for LLMs: different pretrained models exhibit varying reasoning abilities, and no single model performs consistently well across all vulnerability types or contract structures. These limitations persist even after fine-tuning individual LLMs. To address these challenges, LLMBugScanner combines domain knowledge adaptation with ensemble reasoning to improve robustness and generalization. Through domain knowledge adaptation, we fine-tune LLMs on complementary datasets to capture both general code semantics and instruction-guided vulnerability reasoning, using parameter-efficient tuning to reduce computational cost. Through ensemble reasoning, we leverage the complementary strengths of multiple LLMs and apply a consensus-based conflict resolution strategy to produce more reliable vulnerability assessments. We conduct extensive experiments across multiple popular LLMs and compare LLMBugScanner with both pretrained and fine-tuned individual models. Results show that LLMBugScanner achieves consistent accuracy improvements and stronger generalization, demonstrating that it provides a principled, cost-effective, and extensible framework for smart contract auditing.

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