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

arXiv: https://arxiv.org/abs/2512.08326 · HTML: https://mlsanity.com/paper/2512.08326/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 18 of 18)

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- [RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance](https://mlsanity.com/paper/2410.01242/) (2024)
- [LLM-based Control Code Generation using Image Recognition](https://mlsanity.com/paper/2311.10401/) (2023)
- ["False negative -- that one is going to kill you": Understanding Industry Perspectives of Static Analysis based Security Testing](https://mlsanity.com/paper/2307.16325/) (2023)
- [SecretBench: A Dataset of Software Secrets](https://mlsanity.com/paper/2303.06729/) (2023)
- [A Comparative Study of Software Secrets Reporting by Secret Detection Tools](https://mlsanity.com/paper/2307.00714/) (2023)
- [RefleXGen:The unexamined code is not worth using](https://mlsanity.com/paper/2510.23674/) (2025)
- [Don't Leak Your Keys: Understanding, Measuring, and Exploiting the AppSecret Leaks in Mini-Programs](https://mlsanity.com/paper/2306.08151/) (2023)
- [RA-Gen: A Controllable Code Generation Framework Using ReAct for Multi-Agent Task Execution](https://mlsanity.com/paper/2510.08665/) (2025)

## Abstract (arXiv)

Sensitive information leakage in code repositories has emerged as a critical security challenge. Traditional detection methods that rely on regular expressions, fingerprint features, and high-entropy calculations often suffer from high false-positive rates. This not only reduces detection efficiency but also significantly increases the manual screening burden on developers. Recent advances in large language models (LLMs) and multi-agent collaborative architectures have demonstrated remarkable potential for 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 2.2. All code implementations and related datasets are publicly available at https://github.com/TheBinKing/Argus-Guard for further research and application.