Enterprise AI Analysis
Argus: A Multi-Agent Sensitive Information Leakage Detection Framework Based on Hierarchical Reference Relationships
Argus addresses the critical challenge of sensitive information leakage in code repositories by proposing a multi-agent collaborative framework. It employs a three-tier detection mechanism that integrates key content, file context, and project reference relationships, significantly reducing false positives and enhancing overall detection accuracy. Benchmarks show 94.86% accuracy, 96.36% precision, and 94.64% recall, outperforming traditional and LLM-based methods. The framework ensures stability and cost-efficiency.
Key Performance Indicators
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Deep Analysis & Enterprise Applications
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Problem & Motivation
Methodology
Performance & Cost
Sensitive information leakage in code repositories is a critical security challenge. Traditional detection methods (regex, fingerprint, entropy) suffer from high false-positive rates (over 80% for some tools), significantly increasing manual screening burden. Recent LLM and multi-agent advances offer new detection avenues.
Argus responds to these challenges with a multi-agent collaborative framework that integrates key content, file context, and project reference relationships for effective false-positive reduction and accuracy enhancement.
Argus employs a three-tier detection mechanism: Level 1 (Intrinsic Semantics), Level 2 (Immediate Contextual Analysis), and Level 3 (Global Reference Analysis). This is achieved through a multi-agent system with roles: Initial Screening Agent, Commander, Basic Check Agent (Key Format, Placeholder Detection), and Advanced Check Agent (Context Checking, Reference Checking). A shared memory pool facilitates communication.
The system integrates LLMs with specific tools for precise verification, performing secondary analysis and inference to determine key authenticity.
Argus achieves 94.86% accuracy, 96.36% precision, 94.64% recall, and an F1 score of 0.955 on the CommonLeak dataset, significantly outperforming existing methods (GPT-40 direct: 75.26% accuracy, TruffleHog: 57.7%).
The framework demonstrates high stability across different key types and languages. Its total detection cost for 97 real repositories was only $2.21, requiring 68 minutes for single-threaded execution, demonstrating excellent computational efficiency.
Enterprise Process Flow
Initial Screening (TruffleHog)
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Tier 1 Message Pool
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Commander Decision
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Basic Check Agent (Level 1)
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Tier 2 Message Pool
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Commander Decision
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Advanced Check Agent (Level 2/3)
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Tier 3 Message Pool
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Final Determination
Overall Detection Accuracy
94.86% Accuracy Rate Achieved by ArgusArgus significantly outperforms traditional tools and standalone LLMs in accurately identifying sensitive information leaks. This high accuracy is maintained across diverse secret types and programming languages due to its multi-tier contextual analysis.
Argus vs. Baseline Tools: Key Advantages
Argus's multi-agent architecture provides a holistic and accurate approach to secret detection, addressing the shortcomings of both traditional rule-based tools and direct LLM applications.
| Feature | Traditional Regex Tools (e.g., TruffleHog) | LLM-based (e.g., GPT-4 direct) | Argus (Multi-Agent) |
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| Contextual Understanding |
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| Format Verification |
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| Project-level Relationships |
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Detection Cost for 97 Repositories
Value: $2.21
Despite leveraging a multi-agent LLM system for extensive relational analysis, Argus demonstrates excellent computational efficiency. Its use of an information pool for inter-agent data sharing and summary compression substantially reduces redundant computation. The total cost for scanning 97 real-world code repositories was only $2.21, with a single-threaded execution time of 68 minutes, making the advanced detection capabilities highly practical and acceptable for enterprise deployment.
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Your AI Implementation Roadmap
A structured approach to integrating Argus into your security pipeline, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Assessment
Comprehensive analysis of existing security practices, infrastructure, and sensitive data types to tailor Argus for your specific needs.
Phase 2: Pilot Deployment & Integration
Initial deployment of Argus within a controlled environment, integrating with existing code repositories and CI/CD pipelines. Fine-tuning detection rules.
Phase 3: Full-Scale Rollout & Training
Complete deployment across all designated repositories, accompanied by developer training and ongoing support to maximize adoption and effectiveness.
Phase 4: Continuous Optimization & Monitoring
Regular monitoring of Argus's performance, continuous feedback loops, and iterative refinement of detection models to adapt to evolving threats and codebases.
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