About CVE.ICU

🌐 Platform Mission & Capabilities

🎯 Where Vulnerability Chaos Meets Clarity

CVE.ICU embodies RogoLabs' core mission of transforming overwhelming vulnerability data into clear, actionable intelligence that security teams can actually use.

Built on the principle that the best security happens when practical tools are shared freely with the community, this platform cuts through the noise of endless CVE feeds to help security professionals perceive what matters, prioritize what's critical, and protect what counts. No vendor lock-in, no hidden costs—just effective, open-source vulnerability intelligence for everyone.

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Real-Time Analytics

Live vulnerability intelligence with automated 6-hour refresh cycles and comprehensive statistical analysis.

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Deep Insights

Advanced CVSS analysis, CNA intelligence, CWE patterns, and growth trend identification across 27 years of data.

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Free & Open

Completely free platform democratizing cybersecurity intelligence for professionals, researchers, and organizations.

🏗️ Technical Architecture

🔧 Core Technology Stack

Python 3.9+ Pandas NumPy Bootstrap 5 Chart.js D3.js Jinja2 GitHub Pages

📊 Data Sources

Primary: NIST National Vulnerability Database (NVD)
Secondary: CVE Project V5 Repository
Coverage: 287,800+ CVEs across 27 years
Validation: Multi-source cross-verification

⚙️ Processing Engine

Architecture: Static site generation
Analytics: Advanced statistical computation
Storage: Optimized JSON serialization
Updates: Automated 6-hour refresh cycles

📈 Statistical Analysis Framework

✓ Temporal Analysis: Time-series decomposition and trend identification
✓ Distribution Analysis: CVSS score distributions and outlier detection

✓ Correlation Analysis: CWE-CVSS relationships and vulnerability clustering
✓ Predictive Modeling: Growth forecasting and pattern prediction

📊 Data Quality & Coverage

📈 Coverage Statistics

✓ 287,800+ CVE entries across 27 years (1999-2025)
✓ 284,181 CVSS scored vulnerabilities

✓ 206,053 CWE classified weaknesses
✓ 290 active CNAs with attribution data

🎯 Dataset Completeness

Comprehensive: Complete historical dataset
Current: Real-time updates every 6 hours
Validated: Multi-source cross-verification
Accurate: 100% CNA classification accuracy

🔍 Data Validation

Integrity: Automated consistency checks
Quality: Real-time freshness indicators
Reliability: Graceful error handling
Precision: Statistical accuracy controls

⚡ Processing Speed

Optimized: High-performance data structures
Cached: Smart caching mechanisms
Scalable: Modular analysis pipeline
Efficient: Compressed JSON serialization

⚙️ Technical Implementation

🏗️ Architecture Overview

✓ Static Site Generation with Python 3.9+ backend
✓ 7 Analysis Modules for comprehensive intelligence

✓ 6 Intelligence Dashboards with interactive visualizations
✓ Automated 6-hour refresh cycles

🐍 Backend Architecture

Python 3.9+: Type hints and modern features
Pandas/NumPy: Statistical computation engine
Modular Pipeline: Dependency-managed analysis
Performance: Optimized data structures

🎨 Frontend Technology

Bootstrap 5: Responsive design system
Chart.js/D3.js: Interactive visualizations
Vanilla JS: Modern ES6+ features
Mobile-First: Responsive breakpoints

🎛️ Intelligence Dashboard Suite

📊 Dashboard Overview

CVE.ICU provides six specialized intelligence dashboards, each implementing advanced analytical methodologies for comprehensive vulnerability intelligence:

✓ CNA Intelligence: 290 active numbering authorities
✓ CVSS Analysis: Multi-version score distribution
✓ CWE Classification: 68 unique weakness types

✓ CPE Technology: Vendor and platform analysis
✓ Growth Intelligence: 27 years of trend analysis
✓ Calendar Heatmap: Daily publication patterns

🏢 CNA Intelligence

Source: CVE Project V5 Repository
Accuracy: 100% classification via authoritative data
Analysis: Activity patterns and quality metrics

🎯 CVSS Analysis

Versions: v2.0, v3.0, v3.1, v4.0 support
Precision: 0.1-point score accuracy
Coverage: 284,181 scored vulnerabilities

🔍 CWE Intelligence

Taxonomy: MITRE CWE classification
Coverage: 206,053 classified CVEs
Analysis: Weakness pattern identification

💻 CPE Technology

Format: CPE 2.3 platform identifiers
Analysis: Vendor risk assessment
Impact: Technology vulnerability density

📈 Growth Intelligence

Modeling: Year-over-year growth analysis
Methods: 3-year moving averages
Precision: Day-of-year YTD calculations

📅 Calendar Heatmap

Engine: Custom D3.js visualization
Data: 7,658 days of historical data
Features: Interactive year navigation

📡 Data Access & Export Capabilities

📊 Export & Integration

✓ CSV/JSON Export: Multiple format support with UTF-8 encoding
✓ Real-time Access: Direct JSON endpoint integration

✓ 6-hour Updates: Automated refresh cycles with timestamps
✓ 99.9% Uptime: Enterprise-grade availability

📊 Export Formats

CSV: Tabular data with proper delimiters
JSON: Structured data with nested objects
Bulk Downloads: Complete dataset exports
Filtered: Subset data extraction

🔄 Data Freshness

Updates: 6-hour automated cycles
Timestamps: Precise freshness indicators
Detection: Incremental delta processing
Validation: Automated consistency checks

🔗 Integration

REST-like: Direct JSON file access
CORS: Cross-origin resource sharing
Caching: HTTP optimization headers
Security: HTTPS-only with headers

🧮 Statistical Methodology & Scoring

📊 Analytical Rigor

✓ Advanced Statistics: Descriptive and inferential analysis
✓ Multi-version CVSS: Precision score extraction and mapping

✓ Validation Framework: Cross-source integrity checks
✓ Transparency: Reproducible methodologies

📊 Statistical Methods

Descriptive: Mean, median, standard deviation
Distribution: Histogram binning, percentiles
Trend Analysis: Regression, moving averages
Time Series: Pattern identification

🎯 Scoring Algorithms

CVSS Parsing: Multi-version extraction
Severity Mapping: Version-specific thresholds
Normalization: Cross-version standardization
Quality Metrics: Completeness indicators

✓ Validation Framework

Data Integrity: Cross-source validation
Statistical: Outlier detection
Temporal: Chronological consistency
Quality Scoring: Confidence intervals

Transparency Note:

All statistical calculations, data processing methodologies, and scoring algorithms used in CVE.ICU are designed for transparency and reproducibility. The platform prioritizes accuracy, consistency, and scientific rigor in vulnerability intelligence analysis.

🎯 Use Cases & Applications

👥 Target Audiences

✓ Security Teams: Threat landscape analysis and risk assessment
✓ Researchers: Academic studies and statistical analysis

✓ Organizations: Enterprise risk management and compliance
✓ Analysts: Market intelligence and trend reporting

🛡️ Security Teams

Threat Analysis: Landscape monitoring
Risk Assessment: Vulnerability trends
Posture Evaluation: Security metrics

🔬 Researchers

Academic Research: Vulnerability studies
Statistical Analysis: Data projects
Publications: Data sourcing

🏢 Organizations

Risk Management: Enterprise security
Compliance: Reporting requirements
Vendor Assessment: Technology analysis

📊 Analysts

Market Intelligence: Industry insights
Competitive Analysis: Trend reporting
Data-Driven: Strategic insights

📞 Contact & Support

🌐 Community Platform

✓ Open Platform: Serving the cybersecurity community
✓ Operational Status: Continuously updated

✓ 6-hour Updates: Fresh data every cycle
✓ Community Driven: Feedback welcome

💬 Community & Support

CVE.ICU is an open platform designed to serve the cybersecurity community. The platform welcomes feedback, suggestions, and collaboration opportunities.

GitHub Repository: rogolabs/cve.icu
Issues & Feedback: Report Issues
Status: Operational with 6-hour updates

🤝 Contributing

Interested in contributing to CVE.ICU? The platform continuously seeks ways to improve functionality and expand analytical capabilities.

Pull Requests: Submit PRs
Discussions: Join Discussions
Documentation: View README
License: Open Source

🚀 Automated Intelligence Platform

CVE.ICU operates on a fully automated infrastructure that ensures continuous data availability and seamless expansion. The system is engineered to automatically incorporate new vulnerability data as it becomes available, requiring zero manual intervention for ongoing operations.