We actively support and provide security updates for the following versions of AI Society:

AI Society implements several security measures to protect users and their data:

• Local Processing: All model inference happens locally - no data sent to external services
• No Data Persistence: User queries and responses are not stored permanently
• Memory Cleanup: Sensitive data is cleared from memory after processing

• Local-First: Web interface runs on localhost by default
• CORS Protection: Cross-origin requests are properly validated
• Input Validation: All user inputs are validated and sanitized

• Verified Models: Only models from trusted sources (Ollama library)
• Resource Limits: GPU memory and model size constraints prevent resource exhaustion
• Sandboxing: Models run in isolated Ollama environment

We take security vulnerabilities seriously. If you discover a security issue, please follow these steps:

Security vulnerabilities should not be reported through public GitHub issues.

Use GitHub's private vulnerability reporting feature:

1. Go to the repository's Security tab
2. Click "Report a vulnerability"
3. Fill out the vulnerability report form

Or create a private issue with the security label for sensitive reports.

• Type of vulnerability (e.g., injection, authentication bypass, etc.)
• Step-by-step reproduction instructions
• Affected versions of AI Society
• Potential impact of the vulnerability
• Suggested fix (if you have one)
• Your contact information for follow-up questions

• Acknowledgment: We'll acknowledge receipt within 24 hours
• Assessment: We'll assess the vulnerability within 72 hours
• Updates: We'll provide regular updates on our progress
• Resolution: We aim to resolve critical vulnerabilities within 7 days
• Credit: We'll credit you in our security acknowledgments (if desired)

1. Verify Downloads: Always download from official sources
2. Check Signatures: Verify release signatures when available
3. Use Virtual Environments: Install in isolated Python environments
4. Keep Updated: Regularly update to the latest version

1. Network Configuration:

   • Keep web interface on localhost unless needed
   • Use firewall rules to restrict access
   • Monitor network connections

2. Resource Monitoring:

   • Monitor GPU memory usage
   • Set appropriate model size limits
   • Watch for unusual resource consumption

3. Access Control:

   • Restrict file system access
   • Use appropriate user permissions
   • Avoid running as root/administrator

1. Secure Configuration:

   {
     "max_model_size": "8GB",
     "auto_download": false,
     "restricted_mode": true,
     "log_queries": false
   }

2. Environment Variables:

   • Don't commit sensitive config to version control
   • Use environment variables for sensitive settings
   • Regularly rotate any credentials

We encourage security researchers to test AI Society responsibly:

In Scope:

• Web application security (XSS, CSRF, injection attacks)
• API endpoint security
• Authentication and authorization bypasses
• Local file access vulnerabilities
• Resource exhaustion attacks
• Model inference security

Out of Scope:

• Physical attacks
• Social engineering
• Denial of service attacks that require excessive resources
• Issues in third-party dependencies (report to respective projects)

1. Don't harm others: Only test on your own installations
2. Respect resources: Don't consume excessive computational resources
3. Follow disclosure: Use responsible disclosure practices
4. Document thoroughly: Provide clear reproduction steps

Our development team follows these security practices:

• Input Validation: All inputs are validated and sanitized
• Dependency Management: Regular dependency updates and vulnerability scanning
• Code Review: All code changes undergo security review
• Static Analysis: Automated security scanning in CI/CD

• Access Control: Minimal access principles for development infrastructure
• Monitoring: Continuous monitoring for security events
• Backup: Secure backup and recovery procedures
• Incident Response: Defined procedures for security incidents

We thank the following security researchers for responsibly disclosing vulnerabilities:

No vulnerabilities reported yet - be the first to help improve AI Society's security!

Before submitting code, ensure:

• Input validation is implemented for all user inputs
• No sensitive data is logged or persisted
• Error messages don't leak sensitive information
• Dependencies are up to date and don't have known vulnerabilities
• Code follows secure coding practices
• No hardcoded credentials or secrets
• Proper error handling prevents information disclosure

• Safety: Check Python dependencies for known vulnerabilities

  pip install safety
  safety check

• Bandit: Security linter for Python

  pip install bandit
  bandit -r src/

• Semgrep: Static analysis for security issues

  pip install semgrep
  semgrep --config=auto src/

Monitor your AI Society installation:

# Basic security monitoring
import psutil
import logging

def monitor_security():
    # Check for unusual network connections
    connections = psutil.net_connections()
    for conn in connections:
        if conn.status == 'ESTABLISHED':
            logging.info(f"Active connection: {conn.laddr} -> {conn.raddr}")
    
    # Monitor resource usage
    cpu_percent = psutil.cpu_percent()
    if cpu_percent > 90:
        logging.warning(f"High CPU usage: {cpu_percent}%")

If you suspect a security incident:

1. Isolate: Disconnect from network if necessary
2. Preserve: Don't delete logs or evidence
3. Report: Contact security team immediately
4. Document: Record all relevant information
5. Update: Apply security patches as soon as available

• Security Reports: Use GitHub's private vulnerability reporting feature
• Security Team: @dexmac221 (project maintainer)
• Response Time: Within 24 hours for critical issues

This security policy is reviewed and updated regularly. Last updated: September 4, 2025

For the latest version, always check: https://github.com/dexmac221/AiSociety/blob/main/SECURITY.md