We actively support the following versions of the MASTERY-AI Framework with security updates:

• Current Major Version: Full security and bug fix support
• Previous Major Version: Security fixes only for 12 months
• Legacy Versions: No security support (upgrade recommended)

We take the security of the MASTERY-AI Framework seriously. If you discover a security vulnerability, please follow our responsible disclosure process.

Do not report security vulnerabilities through public GitHub issues.

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

1. GitHub Security Advisories (Preferred)

   • Go to the Security tab
   • Click "Report a vulnerability"
   • Fill out the security advisory form

2. Direct Contact

   • Email: security@mastery-ai-framework.org (when available)
   • Subject: "SECURITY: [Brief Description]"

3. Encrypted Communication

   • For highly sensitive reports, request our PGP key
   • Encrypted reports ensure confidentiality during investigation

Please include as much of the following information as possible:

Vulnerability Details:

• Type of vulnerability (injection, authentication bypass, etc.)
• Framework component affected (core, API, pillar-specific, etc.)
• Attack vector and exploitation method
• Potential impact assessment
• Affected versions

Reproduction Information:

• Step-by-step reproduction instructions
• Minimal test case or proof of concept
• Environment details (Python version, OS, dependencies)
• Assessment input data that triggers the vulnerability

Assessment Context:

• Which pillar(s) or factor(s) are affected
• Impact on assessment accuracy or security
• Data exposure risks
• Potential for malicious manipulation of scores

We aim to respond to security reports according to this timeline:

• Initial Response: Within 48 hours
• Triage and Assessment: Within 5 business days
• Fix Development: Varies by severity (see below)
• Release and Disclosure: Coordinated with reporter

Critical Vulnerabilities:

• Assessment manipulation affecting all 148 factors
• Arbitrary code execution through assessment input
• Complete bypass of framework validation
• Mass data exfiltration from assessment results

High Vulnerabilities:

• Manipulation of pillar weights or scoring logic
• Authentication bypass in API endpoints
• Injection attacks through content analysis
• Privilege escalation in multi-tenant deployments

Medium Vulnerabilities:

• Information disclosure of assessment methodology
• Partial bypass of input validation
• DoS attacks on assessment engine
• Insecure default configurations

Low Vulnerabilities:

• Minor information leakage in logs
• Non-exploitable edge cases
• Informational security improvements

Input Validation:

• Comprehensive validation of assessment input data
• Sanitization of user-provided content
• Schema validation for API requests
• Rate limiting on assessment endpoints

Authentication & Authorization:

• API key-based authentication (where applicable)
• Role-based access control for different operations
• Secure session management
• Configurable access controls

Data Protection:

• No data retention by default
• Encrypted data transmission (HTTPS required)
• Secure handling of sensitive assessment data
• Anonymization options for assessment results

Infrastructure Security:

• Secure defaults in configuration
• Protection against common web vulnerabilities
• Audit logging for security events
• Regular security dependency updates

Assessment Security:

• Validation of framework mathematical consistency
• Protection against scoring manipulation
• Secure handling of proprietary assessment factors
• Integrity checks for framework components

API Security:

• Input validation for all endpoints
• Protection against injection attacks
• Secure error handling (no information leakage)
• Request/response logging for audit trails

Deployment Security:

# Use secure configuration
from mastery_ai import Config, AssessmentEngine

config = Config()
config.security.require_https = True
config.security.api_key_required = True
config.logging.audit_enabled = True
config.data.retention_disabled = True

engine = AssessmentEngine(config)

API Security:

# Secure API usage
headers = {
    'Authorization': 'Bearer your-api-key',
    'Content-Type': 'application/json'
}

# Validate and sanitize input
assessment_data = validate_input(raw_data)
response = requests.post(
    'https://your-api.com/assess',
    headers=headers,
    json=assessment_data,
    verify=True  # Verify SSL certificates
)

Secure Coding:

• Validate all inputs at API boundaries
• Use parameterized queries for database operations
• Implement proper error handling without information leakage
• Follow principle of least privilege
• Regular security testing and code reviews

Dependency Management:

• Keep all dependencies updated
• Regular security scanning of dependencies
• Use dependency pinning for production deployments
• Monitor for security advisories

We follow a coordinated disclosure process:

1. Report Received: Acknowledge receipt within 48 hours
2. Investigation: Technical team investigates and confirms
3. Fix Development: Develop and test security fix
4. Coordination: Work with reporter on disclosure timeline
5. Release: Deploy fix and publish security advisory
6. Public Disclosure: Full details after users have time to update

Security advisories are published through:

• GitHub Security Advisories
• Framework documentation
• Release notes with security sections
• Notification to registered users (when available)

For critical security issues, we may provide:

• Automatic security updates for supported versions
• Emergency patches outside regular release cycles
• Hotfixes for production deployments
• Migration tools for breaking security changes

Users are responsible for:

• Monitoring security advisories
• Testing updates in staging environments
• Applying security patches promptly
• Following security best practices

The MASTERY-AI Framework aims to comply with:

• OWASP Top 10 security recommendations
• NIST Cybersecurity Framework guidelines
• Industry-specific security requirements
• Data protection regulations (GDPR compliance)

We conduct regular security assessments:

• Static code analysis
• Dependency vulnerability scanning
• Penetration testing (annual)
• Third-party security reviews

• Primary Contact: security@mastery-ai-framework.org
• GitHub Security: Use Security tab for vulnerability reports
• Emergency Contact: Available for critical vulnerabilities

• Report non-sensitive security improvements through GitHub issues
• Participate in security discussions
• Contribute to security documentation
• Help educate other users about security best practices

We recognize security contributors through:

• Security acknowledgments in release notes
• Contributor recognition in documentation
• CVE credits where applicable
• Community security champion recognition

Security researchers who responsibly disclose vulnerabilities may be listed in our security hall of fame (with permission).

We support security research conducted in good faith:

• Research on your own MASTERY-AI Framework installation
• Responsible disclosure of findings
• No malicious exploitation of vulnerabilities
• Respect for user privacy and data protection

This security policy applies to:

• The MASTERY-AI Framework core library
• Official API endpoints and implementations
• Framework documentation and examples
• Official deployment tools and scripts

Out of Scope:

• Third-party implementations using the framework
• User-specific deployments and configurations
• Social engineering attacks
• Physical security of user installations

Thank you for helping keep the MASTERY-AI Framework and our community secure!

Last updated: January 2025