We actively support the following versions of ARKHE Framework with security updates:

Note: We recommend always using the latest version to receive security updates and bug fixes.

We take security vulnerabilities seriously. If you discover a security vulnerability in ARKHE Framework, please follow these steps:

• Security vulnerabilities should be reported privately to prevent exploitation

• Email: monigarr@MoniGarr.com
• Subject: [SECURITY] ARKHE Framework Vulnerability Report
• Include:
  • Description of the vulnerability
  • Steps to reproduce
  • Potential impact
  • Suggested fix (if any)
  • Your contact information

• Initial Response: Within 48 hours
• Status Update: Within 7 days
• Resolution Timeline: Depends on severity (see below)

• We will acknowledge receipt of your report
• We will investigate and verify the vulnerability
• We will work on a fix and coordinate disclosure
• We will credit you in the security advisory (unless you prefer to remain anonymous)
• Public disclosure will occur after a fix is available

1. Keep Dependencies Updated

   pip install --upgrade -r requirements.txt

2. Use Virtual Environments

   • Always use isolated Python environments
   • Never install packages globally

3. Review Configuration Files

   • Don't commit sensitive data to version control
   • Use environment variables for secrets
   • Review YAML configuration files before use

4. Validate Input Data

   • The framework includes input validation utilities
   • Always validate user input before processing
   • Use the provided validators in math_research.utils.validators

5. Secure Model Files

   • Don't share trained model checkpoints publicly if they contain sensitive data
   • Use secure storage for model artifacts

1. Dependency Scanning

   • Regularly update dependencies
   • Monitor for known vulnerabilities
   • Use tools like safety or pip-audit:

     pip install safety
     safety check -r requirements.txt

2. Code Review

   • All security-related changes require review
   • Pay special attention to:
     • Input validation
     • File I/O operations
     • Network operations
     • Authentication/authorization

3. Secure Defaults

   • Use secure defaults in configuration
   • Don't log sensitive information
   • Sanitize error messages

1. No Authentication/Authorization

   • The framework does not include built-in authentication
   • Users must implement their own security layers for production deployments

2. Input Validation

   • Basic validation is provided, but users should add additional validation for their use cases
   • Large input values may cause resource exhaustion

3. Model Security

   • Trained models may contain information about training data
   • Be cautious when sharing model files

4. Dependencies

   • Some dependencies (e.g., PyTorch) may have their own security considerations
   • Review dependency security advisories regularly

Security updates will be:

• Released as patch versions (e.g., 0.1.1, 0.1.2)
• Documented in CHANGELOG.md under "Security" section
• Tagged with [SECURITY] in commit messages
• Announced via GitHub releases

We recommend:

• Regular dependency audits using pip-audit or safety
• Code security scanning using tools like bandit:

  pip install bandit
  bandit -r src/

• Keeping Python and system packages updated

For security-related questions or concerns:

• Email: monigarr@MoniGarr.com
• Website: MoniGarr.com

We appreciate the security research community's efforts to keep ARKHE Framework secure. Security researchers who responsibly disclose vulnerabilities will be credited in our security advisories.

Last Updated: 2025-01-09
Version: 0.1.0