With the rapid proliferation of the open-source RISC-V architecture, its openness and modular design foster a thriving hardware ecosystem while simultaneously posing significant challenges to hardware security assurance. Early detection of security vulnerabilities during the initial stages of the hardware design flow enables vulnerabilities to be eliminated at minimal cost before being permanently embedded into physical silicon. Although static analysis techniques are applied to early-stage hardware security vulnerability detection, existing methods suffer from high false negative rate (FNR) and false discovery rate (FDR) due to insufficient utilization of specification knowledge and an inadequate semantic understanding of code context. To address these challenges, this study proposes MARC, an early detection method for hardware security vulnerabilities based on collaborative multi-agent systems powered by large language models (LLMs). The proposed method constructs a collaborative framework consisting of four specialized agents: design dependency analysis, documentation analysis, security vulnerability detection, and security vulnerability confirmation. Through multi-dimensional collaboration, including cross-module context augmentation, structured utilization of module documentation knowledge, rapid preliminary screening of potential vulnerabilities, and deep reasoning-based risk analysis. Experiments conducted on an industrial-grade dataset demonstrate that the MARC framework achieves an FNR of 0.3829 and an FDR of 0.3695, representing reductions of approximately 18.2% and 19.1%, respectively, compared to the baseline method. The proposed framework effectively reduces false positives and improves detection accuracy in early-stage hardware design. Furthermore, its real-world effectiveness is validated by the discovery of a hardware vulnerability that has been assigned a CVE identifier. By its successful application, the authors’ team won the global championship in the HACK@DATE 2025 hardware security competition.