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Agentic-MCP-Scanner

What the project does**

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System Overview

What the project does

A multi‑agent security pipeline that scans GitHub repositories implementing the Model Context Protocol (MCP) and other LLM‑integrated systems, automatically detecting classic code vulnerabilities, MCP‑specific risks, and AI‑related threats, then generates comprehensive markdown (or PDF) security reports.

Key features

  • Multi‑agent architecture (scraper, analyzer, threat‑intel, report generator)
  • GitHub repository ingestion and selective file filtering
  • Real‑time threat intelligence via Firecrawl and external URLs
  • Detection of classic bugs (SQLi, XSS, etc.) plus MCP‑specific issues (prompt injection, tool misuse, memory poisoning, credential harvesting) and LLM attacks (model inversion, data poisoning)
  • CVSS & SSVC scoring, executive summary, mitigation recommendations
  • CLI (`mcpvuln`) and Python API for programmatic use
  • Markdown and optional PDF report output

    • Tech stack

  • Python 3.8+
  • agno (multi‑agent framework)
  • gitingest (repo ingestion)
  • firecrawl (threat intel)
  • google‑generativeai (Gemini LLM) & OpenAI API support
  • fpdf (PDF generation)
  • dotenv for configuration

    • Use case

    Security engineers, DevSecOps teams, or researchers need an automated tool to audit MCP‑based applications and LLM‑enhanced services for both traditional software flaws and emerging AI‑centric vulnerabilities, integrating up‑to‑date threat data and delivering actionable reports.

    Architecture Details

    This system integrates multiple components for a seamless automation flow. Structural interpretation based on project focus:

    Backend Infrastructure

    Core execution layer for robust data processing and API handling.

    AI / Logic Core

    Intelligent decisioning via models or logical workflow rules.

    Tech Stack

    PythonIntegrationAutomationAPIs

    Key Capabilities

    • ▹ Custom workflow execution
    • ▹ Data transformation and routing
    • ▹ Extensible architecture