Argus: A Multi-agent Sensitive Information Leakage Detection Framework Based on Hierarchical Reference Relationships

Sensitive information leakage in code repositories has emerged as a critical security challenge. Traditional detection methods—relying on regular expressions, fingerprint features, and high-entropy calculations-suffer from high false-positive rates, which not only reduce detection efficiency but also significantly increase the manual screening burden on developers. Recent advances in large language models(LLMs) and multi-agent collaborative architectures have demonstrated remarkable potential in tackling complex tasks, offering a novel technological perspective for sensitive information detection. In response to these challenges, we propose Argus, a multi-agent collaborative framework for detecting sensitive information. Argus employs a three-tier detection mechanism that integrates key content, file context, and project reference relationships to effectively reduce false positives and enhance overall detection accuracy. To comprehensively evaluate Argus in real-world repository environments, we developed two new benchmarks—one to assess genuine leak detection capabilities and another to evaluate false-positive filtering performance. Experimental results show that Argus achieves up to 94.86% accuracy in leak detection, with a precision of 96.36%, recall of 94.64%, and an F1 score of 0.955.Moreover, the analysis of 97 real repositories incurred a total cost of only $2.21. All code implementations and related datasets are publicly available at https://github.com/TheBinKing/Argus-Guard for further research and application.