CVE Knowledge Graph & Security Intelligence System with Enhanced RAG
This project combines a comprehensive knowledge graph for structured vulnerability data and relationships with an enhanced Retrieval-Augmented Generation (RAG) system for semantic search. We automate the process of curation, processing and correlation of CVE, CPE, CWE, CAPEC, MITRE ATT&CK, ExploitDB, CISA and other threat intelligence data. ## Integration - `CVE-KGRAG` is used as the threat-intelligence backend for simulation projects such as `APTArena`/`CyGATE`. - Typical outputs consumed by simulation pipelines include exploitability signals, tactic mappings, and vulnerability relationship context. ## Current Statistics (Latest) ### **Knowledge Graph Coverage (1999-2025)** - **190,310 CVEs** with rich metadata (CVSS, affected products, CWE, CAPEC, MITRE mappings) - **124,290 products** from **19,692 vendors** - **458 CWEs**, **428 CAPECs**, **169 MITRE techniques**, **37 MITRE tactics** - **2.4M+ relationships** between entities - **80% have CVSS v3** scores (152,676 CVEs) - **1,060 CVEs** in Known Exploited Vulnerabilities (KEV) list ### **NetworkX Graph Statistics** - **335,178 nodes** (CVEs, Products, Vendors, CWEs, CAPECs) - **1,126,306 edges** (relationships between entities) - **246 vulnerability clusters** based on CWE and product relationships - **Graph density**: 0.000010 (sparse, efficient graph structure) # Quick Start ## **Prerequisites** ```bash # Install Python dependencies pip install -r requirements.txt # Optional: if you have NVIDIA GPU and want CUDA-specific PyTorch wheels, # install torch/torchvision/torchaudio separately from the official PyTorch index. # Install Ollama from https://ollama.ai # Then pull required models: ollama pull llama3.1:8b ollama pull llama3.1:70b # Optional: for higher quality responses ``` ## **Complete Setup Workflow** ### **Step 1: Download CVE Data (1999-2025)** ```bash # Download all CVE data from NVD python scripts/download_all_cves.py ``` ### **Step 2: Process and Build Knowledge Graph** ```bash # Collect and process threat intelligence data python src/collectors/main_collector.py # Process all CVE data with enrichment python src/processors/process_all_cves.py # Parse CPEs and extract products/vendors python src/constructors/run_cpe_extraction.py # Build the knowledge graph (JSON-based, no Neo4j required) python src/constructors/kg_builder_without_neo4j.py ``` ### **Step 3: Start Services & Load Knowledge Graph into Neo4j** ```bash # Start Neo4j and Qdrant (Docker required) docker compose up -d # Bulk-load all KG JSON files into Neo4j (~190K CVEs, 2.4M relationships) python -m src.constructors.neo4j_graph_service --bulk-load # Verify Neo4j counts python -m src.constructors.neo4j_graph_service --stats ``` ### **Step 4: Export Chunks & Build Qdrant Hybrid Index** ```bash # Export structure-aware chunks for all four data types # (CVE by section, MITRE techniques, CAPEC patterns, CWE weaknesses) python -m src.generators.export_kg_for_rag_direct --full # Build Qdrant collections with dense + BM25 sparse vectors (hybrid search) python -m src.generators.rag_system --build ``` ### **Step 5: LLM Training (Optional)** ### **Step 5a: Prepare Training Dataset** ```bash # Create training dataset from knowledge graph python src/training/dataset_preparation.py # Analyze data quality if needed python src/training/run_data_analysis.py ``` ### **Step 5b: Check System Requirements** ```bash # Verify system can handle training python src/training/system_check.py ``` ### **Step 5c: Run Fine-Tuning** ```bash python src/training/production_training.py ``` ### **Step 5d: Test Fine-Tuned Model** ```bash # Test the fine-tuned model python src/training/hf_inference_engine.py ``` ### **Step 6: Start Services** ```bash # Terminal 1: Start API Server python -m uvicorn src.api.main:app --host 0.0.0.0 --port 8000 --reload # Terminal 2: Start Gradio UI (Optional) python src/ui/gradio_app.py ``` ## **Test the System** ```bash # Hybrid search — CVE collection (dense + BM25 sparse, RRF fusion) python -m src.generators.rag_system --search "Log4j JNDI injection" # Lexical wins: exact CVE-ID lookup python -m src.generators.rag_system --search "CVE-2021-44228" # Search MITRE techniques collection python -m src.generators.rag_system --search "T1059 command scripting" --collection mitre # Graph: find related CVEs via Neo4j python -m src.constructors.neo4j_graph_service --similar CVE-2021-44228 # Test API endpoints curl -X POST http://localhost:8000/api/v1/search \ -H "Content-Type: application/json" \ -d '{"query": "SQL injection vulnerabilities", "top_k": 5}' # Graph endpoint curl -X POST http://localhost:8000/api/v1/graph/similar \ -H "Content-Type: application/json" \ -d '{"cve_id": "CVE-2021-44228", "k": 5}' # Cross-collection search curl -X POST http://localhost:8000/api/v1/search/mitre \ -H "Content-Type: application/json" \ -d '{"query": "lateral movement", "top_k": 5}' ``` ## **Access Interfaces** - Gradio UI: http://localhost:7860 - API Documentation: http://localhost:8000/docs - API Health Check: http://localhost:8000/api/v1/health ## Architecture Overview ### **Data Pipeline** ``` Raw CVE Data (NVD) → Processed CVE Data → CPE Extraction → Knowledge Graph → NetworkX Graph → Vector DB ``` ### **Knowledge Graph Structure** ``` Nodes: CVEs, Products, Vendors, CWEs, CAPECs, MITRE Techniques, MITRE Tactics Relationships: CVE→Product, CVE→CWE, CVE→CAPEC, CVE→MITRE, Product→Vendor ``` ### **Enhanced RAG System Architecture** ``` ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Query Input │───▶│ FastAPI API │───▶│ Hybrid Search │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ │ ▼ ▼ ┌─────────────────┐ ┌─────────────────┐ │ Gradio UI │ │ Graph Features │ └─────────────────┘ └─────────────────┘ │ │ ▼ ▼ ┌─────────────────┐ ┌─────────────────┐ │ Qdrant + BM25 │ │ Neo4j Graph DB │ └─────────────────┘ └─────────────────┘ │ │ ▼ ▼ ┌─────────────────┐ ┌─────────────────┐ │ Dense + Sparse │ │ Graph-based │ │ RRF Fusion │ │ Similarity │ └─────────────────┘ └─────────────────┘ │ ▼ ┌─────────────────┐ │ LLM Response │ │ (OpenAI compat)│ └─────────────────┘ ``` ### Configuration - **Main config**: `config.py` - **RAG config**: `src/generators/rag_config.py` - **LLM providers**: `llms/` (factory-based: OpenAI-compatible, Ollama, vLLM) - **Example Cypher queries**: `doc/Neo4j_Queries.md` ### **API Endpoints** - `POST /api/v1/query` - Full RAG queries with LLM responses - `POST /api/v1/search` - Vector search only - `POST /api/v1/summary` - Statistical analysis - `GET /api/v1/health` - System health check