context-confidence-rating 0.1.2

Calculate context-aware confidence scores for security findings

Context Confidence Rating (CCR™)

A lightweight library for calculating context-aware confidence scores for security findings.

CCR helps you understand how much a security scanner actually understands your codebase's risk context—not just whether vulnerabilities exist, but whether they're actually exploitable given your application's architecture, dependencies, and security controls.

🎯 Built by Secuarden - Product Security Intelligence Platform

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🚀 Quick Start

# Install
pip install context-confidence-rating

# Analyze a repository
ccr analyze /path/to/your/repo

# Get CCR for a specific finding
ccr analyze /path/to/repo --file "api/auth.py" --vuln "SQL Injection" --severity "HIGH"

💡 What is CCR?

Context Confidence Rating (CCR™) is a 0-100 score that indicates how well security analysis tools understand your codebase's actual risk context. Higher scores mean:

• ✅ Better understanding of data flows
• ✅ More accurate vulnerability prioritization
• ✅ Fewer false positives to investigate
• ✅ More reliable findings for compliance audits

The Problem It Solves

Traditional security scanners give you:

❌ "Found 500 vulnerabilities" (but 480 are noise)

CCR-enhanced analysis gives you:

✅ "Found 20 exploitable vulnerabilities (CCR 85/100 confidence)"

📊 Usage

Python API

from ccr import ContextAnalyzer

# Initialize analyzer
analyzer = ContextAnalyzer("/path/to/repo")

# Get baseline repository CCR
baseline = analyzer.calculate_repo_baseline_ccr()
print(f"Repository CCR: {baseline.score}/100")

# Calculate CCR for a specific finding
finding = {
    "file": "api/payments.py",
    "vulnerability": "SQL Injection",
    "severity": "HIGH"
}

result = analyzer.calculate_ccr(finding)
print(f"Finding CCR: {result.score}/100 ({result.confidence})")
print(f"Reasoning: {result.reasoning}")

Command Line

# Analyze repository baseline
ccr analyze /path/to/repo

# Verbose output with reasoning
ccr analyze /path/to/repo --verbose

# JSON output for CI/CD integration
ccr analyze /path/to/repo --json

# Analyze specific finding
ccr analyze /path/to/repo \
  --file "src/auth.py" \
  --vuln "Hardcoded Credentials" \
  --severity "CRITICAL"

CI/CD Integration

# .github/workflows/security.yml
name: Security Scan with CCR

on: [pull_request]

jobs:
  security:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Run SAST scanner
        run: semgrep --config=auto --json > findings.json
      
      - name: Calculate Context Confidence
        run: |
          pip install context-confidence-rating
          ccr analyze . --json > ccr-report.json
          
      - name: Check CCR threshold
        run: |
          CCR_SCORE=$(jq '.score' ccr-report.json)
          if [ "$CCR_SCORE" -lt 60 ]; then
            echo "::warning::Low context confidence ($CCR_SCORE/100) - findings may need manual review"
          fi

🔍 What CCR Analyzes

CCR examines your repository for context signals:

Signal	Weight	What It Means
Framework Detection	15%	Understanding of web frameworks (Django, Flask, Express)
Dependency Tracking	15%	Presence of requirements.txt, package.json, etc.
Data Flow Analysis	20%	Ability to trace data through your code
Entry Point Mapping	15%	Understanding of application entry points
Config Awareness	10%	Detection of configuration files
Security Controls	15%	Presence of security policies, CI/CD, CODEOWNERS
Test Coverage	10%	Existence of test files and frameworks

CCR Score Ranges

• 71-100 (High): Strong context understanding - findings highly reliable
• 41-70 (Medium): Moderate context - some findings may need verification
• 0-40 (Low): Limited context - manual review recommended

🎯 Use Cases

1. Vulnerability Triage

Re-rank scanner outputs based on actual exploitability in your codebase.

findings = run_security_scanner()  # Returns 500 findings
for finding in findings:
    ccr_result = analyzer.calculate_ccr(finding)
    if ccr_result.score >= 70 and finding.severity == "HIGH":
        prioritize_for_immediate_fix(finding)

2. Audit Preparation

Show auditors you have strong context understanding.

ccr_result = analyzer.calculate_repo_baseline_ccr()
print(f"Our security analysis has {ccr_result.score}/100 context confidence")
# Demonstrates mature security posture

3. Scanner Comparison

Evaluate which security tools work best for your codebase.

# Tool A gives 500 findings with CCR 45 (low confidence)
# Tool B gives 50 findings with CCR 82 (high confidence)
# → Tool B is more effective for your context

4. CI/CD Quality Gate

Fail builds when context drops below threshold.

ccr analyze . --json | jq '.score' | awk '$1 < 60 {exit 1}'

🛠️ Installation

From PyPI (when published)

pip install context-confidence-rating

From Source

git clone https://github.com/secuardenai/context-confidence-rating.git
cd context-confidence-rating
pip install -e .

Development Installation

git clone https://github.com/secuardenai/context-confidence-rating.git
cd context-confidence-rating
pip install -e ".[dev]"
pytest

📖 Example Output

============================================================
  Context Confidence Rating (CCR™) Analysis
============================================================

📊 CCR Score: 78/100
🎯 Confidence Level: HIGH
   ✓ Strong context understanding - findings highly reliable

📋 Context Signals Detected:
   ✓ Framework Detection (+15 points)
      Frameworks: Flask, SQLAlchemy
   ✓ Dependency Tracking (+15 points)
      Files: requirements.txt, Pipfile.lock
   ✓ Data Flow Analysis (+20 points)
   ✓ Entry Point Mapping (+15 points)
   ✓ Security Controls (+13 points)
      Controls: security_policy, ci_cd_pipeline, code_ownership

💡 Reasoning:
   ✓ Framework Detection
   ✓ Dependency Tracking
   ✓ Data Flow Analysis
   ✓ Entry Point Mapping
   ✓ Config Awareness
   ✓ Security Controls
   ↑ High-severity finding prioritization (+5)

📁 Repository Overview:
   Languages: Python, JavaScript
   Files: 247
   Frameworks: Flask, SQLAlchemy

💡 Recommendations:
   • Excellent context signals detected!
   • Consider integrating CCR into your CI/CD pipeline

============================================================

🔗 Integration with Security Tools

CCR is designed to enhance—not replace—existing security scanners:

• ✅ Semgrep: Enhance SAST findings with context scores
• ✅ Bandit: Add confidence to Python security analysis
• ✅ Snyk: Contextualize dependency vulnerability impact
• ✅ GitHub Security: Prioritize CodeQL/Dependabot alerts
• ✅ Custom Tools: Integrate via JSON output

🤝 Contributing

We welcome contributions! This is an open-source project maintained by Secuarden.

# Setup development environment
git clone https://github.com/secuardenai/context-confidence-rating.git
cd context-confidence-rating
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black ccr/
flake8 ccr/

📝 License

MIT License - see LICENSE file for details.

🙏 Credits

Created by the Secuarden team.

CCR™ (Context Confidence Rating) is a trademark of Appsec360.

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🚀 Want More?

CCR is the open-source foundation of Secuarden - our Product Security Intelligence Platform that:

• 🔍 Transforms generic SAST findings into audit-ready compliance evidence
• 🎯 Prioritizes vulnerabilities using CCR + exploitability analysis
• 📊 Maps findings to SOC 2, ISO 27001, PCI-DSS requirements
• 🤖 Generates AI-powered remediation with context-aware code suggestions
• ✅ Provides PR-level security enforcement with intelligent blocking

Try Secuarden Free →

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Questions? Open an issue or email [e-mail]