We take security seriously. If you discover a security vulnerability in Mobile Multi-Modal LLM, please report it responsibly.

DO NOT create a public issue for security vulnerabilities.

Instead, please email us at: [email protected]

Include the following information:

• Description of the vulnerability
• Steps to reproduce the issue
• Potential impact assessment
• Any suggested fixes or mitigations

• Acknowledgment: Within 48 hours
• Initial assessment: Within 5 business days
• Fix development: Varies by severity
• Public disclosure: After fix is released

We provide security updates for the following versions:

• Adversarial inputs: Models may be vulnerable to adversarial examples
• Data poisoning: Be cautious with untrusted training data
• Model extraction: Protect model weights in production
• Privacy: Ensure on-device processing for sensitive data

• App signing: Always sign mobile applications properly
• Certificate pinning: Implement for API communications
• Root/jailbreak detection: Consider for high-security applications
• Secure storage: Use platform keystore for sensitive data

• Regular updates: Keep dependencies current with security patches
• Vulnerability scanning: Automated scanning with bandit and safety
• License compliance: Ensure compatible licenses for all dependencies

• Pre-commit hooks: Security scanning before commits
• Secrets management: Never commit credentials or API keys
• Code review: All changes require security-aware review
• Access control: Limit repository access and permissions

1. Keep updated: Always use the latest version
2. Verify downloads: Check hashes and signatures
3. Secure deployment: Follow mobile security guidelines
4. Input validation: Sanitize all inputs to the model
5. Error handling: Don't expose internal details in errors

1. Security training: Understand common vulnerabilities
2. Secure coding: Follow OWASP guidelines
3. Dependency review: Audit new dependencies
4. Testing: Include security test cases
5. Documentation: Document security implications

We use the following tools for security:

• bandit: Python security linter
• safety: Python dependency vulnerability scanner
• GitGuardian: Secret detection in commits
• Dependabot: Automated dependency updates
• CodeQL: Static code analysis

• OWASP Top 10: Regular assessment
• NIST Cybersecurity Framework: Risk management
• ISO 27001: Information security management

• Model privacy: Data minimization principles
• Bias mitigation: Fair and unbiased model behavior
• Explainability: Understanding model decisions
• Robustness: Reliable performance under various conditions

For security-related questions:

• Email: [email protected]
• GPG Key: Available on request
• Response SLA: 48 hours for critical issues

For general security guidance:

• Documentation: See Security Guide
• Community: GitHub Discussions