  Large Language Models (LLMs) have strong capabilities in code comprehension,
but fine-tuning costs and semantic alignment issues limit their
project-specific optimization; conversely, code models such CodeBERT are easy
to fine-tune, but it is often difficult to learn vulnerability semantics from
complex code languages. To address these challenges, this paper introduces the
Multi-Model Collaborative Vulnerability Detection approach (M2CVD) that
leverages the strong capability of analyzing vulnerability semantics from LLMs
to improve the detection accuracy of code models. M2CVD employs a novel
collaborative process: first enhancing the quality of vulnerability semantic
description produced by LLMs through the understanding of project code by code
models, and then using these improved vulnerability semantic description to
boost the detection accuracy of code models. We demonstrated M2CVD's
effectiveness on two real-world datasets, where M2CVD significantly
outperformed the baseline. In addition, we demonstrate that the M2CVD
collaborative method can extend to other different LLMs and code models to
improve their accuracy in vulnerability detection tasks.
