VulnGraph AI

Intelligent dependency risk scoring, graph-based reachability analysis, and vulnerability prioritization platform.

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VulnGraph AI

Demo video link : https://www.youtube.com/watch?v=Ga550O0RQOk

Intelligent dependency risk scoring, graph-based reachability analysis, and vulnerability prioritization platform.

VulnGraph AI ingests Software Bill of Materials (SBOMs), models dependencies as a directed graph, applies context-aware risk scoring, ranks vulnerabilities using a pre-trained Random Forest classifier, and outputs actionable remediation recommendations with config file patches.

Features

  • Multi-Format Ingestion: Supports CycloneDX and SPDX JSON formats.
  • Reachability Mapping: Calculates dependency depth and topological statistics (in-degree, out-degree, dependents count) using Breadth-First Search (BFS).
  • Context-Aware Risk Engine: Evaluates CVSS base score, exploit availability, EPSS probability, patch lag (version distance), and path depth decay.
  • Machine Learning Prioritization: Uses a Random Forest Classifier to categorize risk levels (LOW, MEDIUM, HIGH, CRITICAL) with local feature contribution explanations.
  • Configurable Scoring Weights: Offers runtime configuration of engine parameters via API to dynamically adjust organization-specific gates.
  • Actionable Remediation suggestions: Generates package-manager commands (npm/pip) and configuration patches (package.json/requirements.txt diffs) with click-to-remediate simulation.
  • Interactive Dark-themed Dashboard: Built with Cytoscape.js for force-directed node-link visualizations and Chart.js for stats analysis.

Tech Stack

  • Backend: FastAPI (Python 3.9+), SQLAlchemy, SQLite, scikit-learn, pandas, numpy, joblib.
  • Frontend: HTML5, Vanilla CSS (Glassmorphism design system), Vanilla JS, Cytoscape.js, Chart.js.
  • Testing: Pytest, FastAPI TestClient.

Installation & Setup

Follow these steps to run the application locally on Windows:

1. Set Up Virtual Environment & Install Dependencies

Navigate to the backend directory, create a virtual environment, and install the required binary packages:

# Navigate to backend directory
cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
.\venv\Scripts\Activate.ps1

# Install requirements using precompiled binaries
python -m pip install --only-binary :all: -r requirements.txt
python -m pip install httpx

2. Seed Database & Pre-train ML Model

The system seeds SQLite with vulnerability threat intelligence and trains the classifier on startup. Alternatively, you can seed manually:

# Run the database seed script
python -m backend.data.seed_data

3. Run Unit and Integration Tests

Validate the application services and static endpoint routing by running pytest from the project root:

# Run from workspace root directory
cd ..
.\backend\venv\Scripts\python -m pytest backend/tests/

4. Start the Application

Start the FastAPI development server:

.\backend\venv\Scripts\uvicorn backend.main:app --reload --host 127.0.0.1 --port 8000

API Endpoints Reference

1. Configuration & Calibration

  • GET /api/risk/config - Retrieve current risk scoring weights.
  • POST /api/risk/config - Update scoring weights and re-evaluate the database.
    • Request Body: json { "cvss_weight": 10.0, "epss_weight": 1.0, "patch_lag_weight": 1.0, "depth_weight": 1.0 }

2. Remediation Recommendations

  • GET /api/remediation/{finding_id} - Fetch upgrade commands and config patch diffs for a specific vulnerability.
  • POST /api/remediate/{finding_id} - Apply/simulate vulnerability remediation (updates package version in DB).

3. Ingestion & Graph Analytics

  • POST /api/sbom/upload - Upload CycloneDX/SPDX JSON files to analyze software risk.
  • GET /api/repositories - Fetch tracked repository status records.
  • GET /api/graph/{repository_id} - Fetch Graph node-link schema for Cytoscape renderer.
  • GET /api/dashboard - Get high-level KPI and chart metrics.
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