# PyTorch Supply Chain Security

Comprehensive guide to securing the ML model supply chain and validating model sources.

## Model Source Validation

### 1. Trusted Repositories

```python
TRUSTED_SOURCES = [
    'huggingface.co',
    'pytorch.org',
    'github.com/pytorch',
    'your-internal-repo.com'
]

def validate_model_source(url):
    from urllib.parse import urlparse
    domain = urlparse(url).netloc.lower()
    return any(trusted in domain for trusted in TRUSTED_SOURCES)
```

### 2. Cryptographic Verification

```python
import hashlib
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.asymmetric import rsa, padding

def verify_model_signature(model_path, signature_path, public_key):
    """Verify model cryptographic signature"""

    # Read model file
    with open(model_path, 'rb') as f:
        model_data = f.read()

    # Read signature
    with open(signature_path, 'rb') as f:
        signature = f.read()

    # Verify signature
    try:
        public_key.verify(
            signature,
            model_data,
            padding.PSS(
                mgf=padding.MGF1(hashes.SHA256()),
                salt_length=padding.PSS.MAX_LENGTH
            ),
            hashes.SHA256()
        )
        return True
    except Exception:
        return False
```

### 3. Model Registry Integration

```python
class SecureModelRegistry:
    def __init__(self, registry_url, api_key):
        self.registry_url = registry_url
        self.api_key = api_key

    def download_model(self, model_id, verify_signature=True):
        """Download model with security validation"""

        # Get model metadata
        metadata = self.get_model_metadata(model_id)

        # Validate metadata
        if not self.validate_metadata(metadata):
            raise ValueError("Model metadata validation failed")

        # Download model file
        model_path = self.download_file(metadata['download_url'])

        # Verify hash
        if not self.verify_hash(model_path, metadata['sha256']):
            raise ValueError("Model hash verification failed")

        # Verify signature if required
        if verify_signature and not self.verify_signature(model_path, metadata):
            raise ValueError("Model signature verification failed")

        return model_path
```

## Security Implementation Examples

### Hash Verification

```python
import hashlib

def verify_model_hash(model_path, expected_hash):
    """Verify model file integrity using SHA256 hash"""
    with open(model_path, 'rb') as f:
        actual_hash = hashlib.sha256(f.read()).hexdigest()
    return actual_hash == expected_hash

# Usage
if verify_model_hash('model.pt', 'expected_sha256_hash'):
    model = torch.load('model.pt', weights_only=True)
else:
    raise ValueError("Model integrity check failed")
```

### Secure Download with Validation

```python
import requests
import hashlib
from pathlib import Path

def secure_download_model(url, expected_hash, local_path):
    """Securely download and validate model"""

    # Validate source
    if not validate_model_source(url):
        raise ValueError(f"Untrusted model source: {url}")

    # Download with streaming
    response = requests.get(url, stream=True)
    response.raise_for_status()

    # Calculate hash while downloading
    hash_sha256 = hashlib.sha256()
    with open(local_path, 'wb') as f:
        for chunk in response.iter_content(chunk_size=8192):
            hash_sha256.update(chunk)
            f.write(chunk)

    # Verify integrity
    if hash_sha256.hexdigest() != expected_hash:
        Path(local_path).unlink()  # Remove corrupted file
        raise ValueError("Downloaded model failed integrity check")

    return local_path
```

## Supply Chain Threat Mitigation

### 1. Model Provenance Tracking

```python
class ModelProvenance:
    def __init__(self):
        self.history = []

    def add_event(self, event_type, source, timestamp, metadata=None):
        """Track model lifecycle events"""
        self.history.append({
            'event': event_type,
            'source': source,
            'timestamp': timestamp,
            'metadata': metadata or {}
        })

    def validate_chain(self, trusted_sources):
        """Validate complete provenance chain"""
        for event in self.history:
            if event['source'] not in trusted_sources:
                return False
        return True
```

### 2. Model Scanning Integration

```python
from modelaudit import scan_file

def secure_model_pipeline(model_url, expected_hash):
    """Complete secure model loading pipeline"""

    # 1. Download with validation
    local_path = secure_download_model(model_url, expected_hash, 'model.pt')

    # 2. Security scan
    scan_result = scan_file(local_path)
    critical_issues = [issue for issue in scan_result.issues
                      if issue.severity == 'CRITICAL']

    if critical_issues:
        raise ValueError(f"Security issues found: {critical_issues}")

    # 3. Safe loading
    return torch.load(local_path, weights_only=True, map_location='cpu')
```

## Enterprise Security Patterns

### Model Signing Workflow

```python
from cryptography.hazmat.primitives.asymmetric import rsa
from cryptography.hazmat.primitives import serialization, hashes
from cryptography.hazmat.primitives.asymmetric import padding

class ModelSigner:
    def __init__(self, private_key_path, public_key_path):
        self.private_key = self.load_private_key(private_key_path)
        self.public_key = self.load_public_key(public_key_path)

    def sign_model(self, model_path, signature_path):
        """Sign model file with private key"""
        with open(model_path, 'rb') as f:
            model_data = f.read()

        signature = self.private_key.sign(
            model_data,
            padding.PSS(
                mgf=padding.MGF1(hashes.SHA256()),
                salt_length=padding.PSS.MAX_LENGTH
            ),
            hashes.SHA256()
        )

        with open(signature_path, 'wb') as f:
            f.write(signature)

    def verify_model(self, model_path, signature_path):
        """Verify model signature with public key"""
        return verify_model_signature(model_path, signature_path, self.public_key)
```

## Security Monitoring

### Audit Logging

```python
import logging
from datetime import datetime

class ModelSecurityAuditor:
    def __init__(self, log_file='model_audit.log'):
        logging.basicConfig(
            filename=log_file,
            level=logging.INFO,
            format='%(asctime)s - %(levelname)s - %(message)s'
        )
        self.logger = logging.getLogger(__name__)

    def log_model_load(self, model_path, source, validation_status):
        """Log model loading events"""
        self.logger.info(f"Model loaded: {model_path} from {source} - Status: {validation_status}")

    def log_security_scan(self, model_path, scan_results):
        """Log security scan results"""
        issues_count = len(scan_results.issues)
        critical_count = len([i for i in scan_results.issues if i.severity == 'CRITICAL'])

        self.logger.warning(f"Security scan: {model_path} - {issues_count} issues ({critical_count} critical)")
```

## Best Practices Summary

### Development Phase

1. **Use internal model registries** with access controls
2. **Implement model signing** for internal distribution
3. **Maintain provenance tracking** throughout development
4. **Regular security scanning** in development workflow

### Production Deployment

1. **Verify all model signatures** before deployment
2. **Use hash verification** for integrity checks
3. **Implement network-level controls** for model downloads
4. **Monitor and log** all model loading activities

### Incident Response

1. **Quarantine suspicious models** immediately
2. **Analyze provenance chain** for compromise points
3. **Update security controls** based on lessons learned
4. **Communicate findings** to development teams

## Related Guides

- [PyTorch Security Overview](pytorch-overview.md) - Understanding threat vectors
- [Safe Loading Practices](pytorch-safe-loading.md) - Secure loading techniques
- [Security Checklist](pytorch-checklist.md) - Complete security checklist
