# PyTorch Security Checklist

Comprehensive security checklist for PyTorch model development and deployment.

## Before Loading Any PyTorch Model

### Source Validation

- [ ] **Verify Source**: Is the model from a trusted repository?
- [ ] **Check Provenance**: Do you know the complete model history?
- [ ] **Validate Signatures**: Are cryptographic signatures valid?
- [ ] **Review Documentation**: Is model purpose and training data documented?

### File Integrity

- [ ] **Check Hash**: Does the file hash match expected value?
- [ ] **Verify Size**: Is file size within expected range?
- [ ] **Scan with ModelAudit**: Are there any security issues detected?
- [ ] **Check File Type**: Is the file actually a model file?

### Environment Preparation

- [ ] **PyTorch Version**: Are you using PyTorch 2.6.0 or later?
- [ ] **Use weights_only=True**: As a basic precaution (not foolproof)
- [ ] **Isolation**: Is model loading happening in isolated environment?
- [ ] **Backup Systems**: Are critical systems backed up before loading?

### Code Review

- [ ] **Validate Content**: Does the model behavior match expectations?
- [ ] **Review Loading Code**: Is model loading code secure?
- [ ] **Check Dependencies**: Are all dependencies up to date?
- [ ] **Input Validation**: Is user input properly validated?

## For Production Deployments

### Model Registry Security

- [ ] **Signature Verification**: Are models cryptographically signed?
- [ ] **Access Controls**: Are appropriate access controls in place?
- [ ] **Audit Logging**: Are all model operations logged?
- [ ] **Version Control**: Is model versioning properly managed?

### Infrastructure Security

- [ ] **Sandboxing**: Are models loaded in isolated environments?
- [ ] **Network Isolation**: Is model serving network isolated?
- [ ] **Resource Limits**: Are resource limits configured?
- [ ] **Monitoring**: Is model behavior monitored in production?

### CI/CD Integration

- [ ] **Regular Scanning**: Is ModelAudit integrated in CI/CD?
- [ ] **Automated Testing**: Are security tests automated?
- [ ] **Deployment Gates**: Are security gates enforced?
- [ ] **Rollback Procedures**: Are rollback procedures defined?

### Security Updates

- [ ] **Migration Plan**: Moving to SafeTensors format?
- [ ] **Security Updates**: Process for patching vulnerabilities?
- [ ] **Regular Reviews**: Scheduled security reviews in place?
- [ ] **Threat Intelligence**: Staying informed about new threats?

## For TorchScript Models

### Static Analysis

- [ ] **Static Compilation**: Avoid dynamic code generation?
- [ ] **Operation Validation**: No dangerous operations in graph?
- [ ] **Graph Analysis**: Are graphs analyzed for security issues?
- [ ] **Compilation Safety**: Is compilation process secure?

### Runtime Security

- [ ] **Hook Review**: All hooks perform only safe operations?
- [ ] **Input Validation**: Are all inputs properly validated?
- [ ] **Output Sanitization**: Are outputs properly sanitized?
- [ ] **Advanced Scanning**: ModelAudit TorchScript analysis enabled?

### Deployment Considerations

- [ ] **Pre-deployment Scan**: Models scanned before deployment?
- [ ] **Runtime Monitoring**: Inference monitored for security violations?
- [ ] **Circuit Breakers**: Automatic shutdown on suspicious activity?
- [ ] **Incident Response**: Response plan for security incidents?

## For Development Teams

### Training and Awareness

- [ ] **Training**: Team educated on ML security risks?
- [ ] **Documentation**: Security guidelines documented and accessible?
- [ ] **Best Practices**: Development practices follow security guidelines?
- [ ] **Incident Training**: Team trained on incident response?

### Tooling and Process

- [ ] **Policies**: Clear guidelines for model usage?
- [ ] **Tools**: ModelAudit integrated in development workflow?
- [ ] **Code Review**: Security considerations in code reviews?
- [ ] **Testing**: Security testing integrated in test suites?

### Governance

- [ ] **Updates**: Regular security patch management?
- [ ] **Compliance**: Compliance requirements met?
- [ ] **Risk Assessment**: Regular security risk assessments?
- [ ] **Monitoring**: Automated security scanning in place?

## Emergency Response Checklist

### If You Discover a Malicious Model

#### Immediate Actions (First 5 minutes)

- [ ] **Disconnect Network**: Isolate affected systems from network
- [ ] **Kill Processes**: Terminate all Python processes loading the model
- [ ] **Quarantine Files**: Move suspicious model files to quarantine
- [ ] **Alert Team**: Notify security team and stakeholders

#### Investigation (First hour)

- [ ] **System Scan**: Check system for unauthorized changes
- [ ] **Log Analysis**: Review system and application logs
- [ ] **Network Analysis**: Check for suspicious network activity
- [ ] **Identify Scope**: Determine extent of potential compromise

#### Analysis (First 4 hours)

- [ ] **Model Analysis**: Analyze model with ModelAudit in isolated environment
- [ ] **Attack Vector**: Identify how the malicious model was introduced
- [ ] **Impact Assessment**: Assess damage and data exposure
- [ ] **Evidence Collection**: Collect forensic evidence

#### Recovery (First 24 hours)

- [ ] **Clean Systems**: Restore from clean backups if necessary
- [ ] **Update Security**: Implement additional security measures
- [ ] **Patch Systems**: Update PyTorch and all dependencies
- [ ] **Test Systems**: Verify systems are clean and functional

#### Prevention (Following week)

- [ ] **Review Procedures**: Update security policies and procedures
- [ ] **Team Training**: Conduct additional security training
- [ ] **Monitoring Enhancement**: Implement additional monitoring
- [ ] **Incident Documentation**: Document lessons learned

## ModelAudit Integration Checklist

### Basic Integration

- [ ] **Installation**: ModelAudit installed with appropriate extras?
- [ ] **CLI Testing**: Command-line scanning working correctly?
- [ ] **Python API**: Python integration working in codebase?
- [ ] **Configuration**: ModelAudit configured for your environment?

### Advanced Features

- [ ] **Custom Patterns**: Organization-specific threat patterns added?
- [ ] **Threshold Tuning**: Detection thresholds tuned for your use case?
- [ ] **Output Format**: JSON output configured for automation?
- [ ] **Timeout Settings**: Appropriate timeouts configured for model sizes?

### Automation

- [ ] **Pre-commit Hooks**: Automated scanning on code commits?
- [ ] **CI/CD Pipeline**: Security scanning integrated in deployment pipeline?
- [ ] **Registry Integration**: Model registry integrated with scanning?
- [ ] **Monitoring**: Continuous monitoring configured?

### Reporting and Alerting

- [ ] **Metrics Collection**: Security metrics being collected?
- [ ] **Alert Configuration**: Alerts configured for critical issues?
- [ ] **Report Generation**: Regular security reports generated?
- [ ] **Audit Trail**: Complete audit trail maintained?

## Compliance and Governance

### Documentation Requirements

- [ ] **Security Policy**: Written security policy for ML models?
- [ ] **Procedure Documentation**: Procedures documented and current?
- [ ] **Training Records**: Security training documented?
- [ ] **Incident Records**: Security incidents properly documented?

### Risk Management

- [ ] **Risk Assessment**: Regular security risk assessments conducted?
- [ ] **Risk Register**: Security risks tracked in risk register?
- [ ] **Mitigation Plans**: Risk mitigation plans in place?
- [ ] **Risk Monitoring**: Ongoing risk monitoring implemented?

### Audit Preparation

- [ ] **Audit Trail**: Complete audit trail available?
- [ ] **Evidence Collection**: Security evidence systematically collected?
- [ ] **Control Testing**: Security controls regularly tested?
- [ ] **Compliance Reporting**: Regular compliance reports generated?

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**Remember**: Security is not a one-time setup but an ongoing process. Regular audits, updates, and vigilance are essential for maintaining secure ML systems.

## Quick Reference

### Critical Security Actions

1. ✅ **Always scan models** with ModelAudit before loading
2. ✅ **Update PyTorch** to version 2.6.0 or later
3. ✅ **Verify model sources** and use trusted repositories
4. ✅ **Implement hash verification** for model integrity
5. ✅ **Use SafeTensors format** when possible
6. ❌ **Never rely solely** on `weights_only=True` for security
7. ❌ **Never load untrusted models** without validation
8. ❌ **Never skip security scanning** in production deployments

### Emergency Contacts

- **Security Team**: [Your security team contact]
- **DevOps Team**: [Your DevOps team contact]
- **Management**: [Incident escalation contact]
- **External Support**: [External security support if needed]
