The Data Analysis Framework is designed with security as a fundamental principle, especially considering its use with AI agents and potentially sensitive data. This document outlines our security practices and how to report vulnerabilities.

• Query Validation: All AI-generated queries are validated before execution
• Safe Query Interface: Restricted operations prevent dangerous data manipulation
• Input Sanitization: All user inputs are sanitized to prevent injection attacks
• Read-Only Operations: Default interface prevents data modification
• Query Complexity Limits: Protection against resource exhaustion attacks

• Local Processing: All analysis happens locally - no data sent to external services
• Memory Management: Secure cleanup of sensitive data from memory
• Temporary File Handling: Secure creation and cleanup of temporary files
• Access Control: File system access limited to specified directories
• PII Detection: Built-in detection of potentially sensitive information

• Dependency Scanning: Regular security audits of dependencies
• Input Validation: Comprehensive validation of all inputs
• Error Handling: Secure error messages that don't leak sensitive information
• Type Safety: Full type checking to prevent runtime errors
• Resource Limits: Protection against memory and CPU exhaustion

We provide security updates for the following versions:

🔒 For security vulnerabilities, please DO NOT open a public GitHub issue.

Instead, please report security vulnerabilities through one of these channels:

1. Email: Send details to wjackson@redhat.com with subject "SECURITY: Data Analysis Framework"
2. GitHub Security Advisory: Use GitHub's private vulnerability reporting feature
3. Encrypted Communication: PGP key available upon request

Please provide as much information as possible:

## Vulnerability Report Template

**Summary**: Brief description of the vulnerability

**Impact**: What could an attacker accomplish?

**Steps to Reproduce**:
1. Step one
2. Step two
3. Step three

**Affected Versions**: Which versions are impacted?

**Environment**: 
- OS: 
- Python version: 
- Package version: 

**Proposed Fix**: If you have suggestions

**Disclosure Timeline**: Your preferred timeline for public disclosure

• Initial Response: Within 48 hours
• Vulnerability Assessment: Within 1 week
• Fix Development: Within 2-4 weeks (depending on severity)
• Public Disclosure: After fix is released and tested

# ✅ Good: Use with trusted, local data files
analyzer = DataAnalyzer()
result = analyzer.analyze("local_data.xlsx")

# ⚠️ Caution: Be careful with remote or untrusted files
# Always validate and scan files from external sources

# ✅ Good: Use safe query interface
interface = create_agent_interface("trusted_data.csv")
result = interface.execute_query("Find customers in Boston")

# ❌ Avoid: Don't disable safety features
# interface.disable_safety_checks()  # Never do this

# ✅ Good: Use virtual environments
# python -m venv venv
# source venv/bin/activate
# pip install data-analysis-framework

# ✅ Good: Keep dependencies updated
# pip install --upgrade data-analysis-framework

• Always validate inputs before processing
• Use type hints and runtime validation
• Handle errors gracefully without exposing sensitive information
• Sanitize file paths to prevent directory traversal
• Limit resource usage to prevent DoS attacks

def test_path_traversal_protection():
    """Test that path traversal attacks are prevented"""
    with pytest.raises(SecurityError):
        analyzer.analyze("../../../etc/passwd")

def test_query_injection_protection():
    """Test that SQL injection is prevented"""
    result = interface.execute_query("'; DROP TABLE users; --")
    assert result.success == False
    assert "Invalid query" in result.warnings

# Restrict file system access
export DAF_ALLOWED_PATHS="/safe/data/directory"

# Enable additional security logging
export DAF_SECURITY_LOGGING=true

# Set memory limits
export DAF_MAX_MEMORY_MB=1024

# Enable safe mode (extra restrictions)
export DAF_SAFE_MODE=true

# Configure security settings
analyzer = DataAnalyzer(
    max_file_size_mb=100,
    allowed_extensions=['.csv', '.xlsx'],
    enable_path_validation=True,
    safe_mode=True
)

• Large Files: Memory exhaustion possible with very large files
• Malformed Files: Specially crafted files could cause crashes
• External References: Excel files with external links are blocked

• Query Complexity: Complex queries may consume significant resources
• Data Leakage: AI models might retain information from queries
• Prompt Injection: Malicious prompts could bypass safety measures

• File size limits and memory monitoring
• Input validation and sanitization
• Resource usage limits and timeouts
• Query complexity analysis
• Safe query execution environment

• GitHub Releases: Security updates included in release notes
• Security Advisories: Critical vulnerabilities announced via GitHub
• Changelog: Security fixes documented in CHANGELOG.md
• Email: Major security updates can be sent to maintainers

# Check for security updates
pip list --outdated | grep data-analysis-framework

# Update to latest secure version
pip install --upgrade data-analysis-framework

# Verify security features
python -c "import data_analysis_framework; print('Security features active')"

We appreciate security researchers who help keep our project safe:

Contributors who have responsibly disclosed vulnerabilities will be listed here with their permission.

While we don't currently offer monetary rewards, we provide:

• Public recognition (with permission)
• Priority support for security-related issues
• Direct communication with maintainers
• Credit in release notes and documentation

For security-related questions:

• Security Issues: wjackson@redhat.com
• General Questions: GitHub Issues (for non-sensitive topics)
• Documentation: GitHub Discussions

Remember: Security is a shared responsibility. Please help us keep the Data Analysis Framework secure for everyone! 🛡️