Model Safety PyTorch TensorFlow MCP

Overview

Model Safety PyTorch TensorFlow MCP is a security-focused Model Context Protocol server for scanning machine learning model artifacts. It specifically supports scanning PyTorch checkpoint files (.pt, .pth) for malicious pickle members and TensorFlow HDF5 artifacts to identify unsafe serialization patterns and potential security risks.

Features

• PyTorch Scanning: Deep inspection of .pt and .pth checkpoint files
• TensorFlow Analysis: Security scanning of HDF5 model artifacts
• Pickle Security: Detect malicious pickle deserialization threats
• Pattern Detection: Identify known malicious code patterns
• URL Scanning: Analyze model artifacts from remote URLs
• Directory Triage: Batch scan entire directories
• Risk Assessment: Severity scoring for detected threats
• Remediation Guidance: Suggestions for fixing security issues

Security Checks

Unsafe Serialization

• Malicious pickle operations
• Code execution vulnerabilities
• Arbitrary file access
• Network requests in models
• Shell command execution

Malicious Patterns

• Known exploit signatures
• Backdoor detection
• Data exfiltration code
• Obfuscated payloads
• Supply chain attacks

Model Integrity

• Tampering detection
• Checksum verification
• Signature validation
• Version compatibility
• Dependency analysis

Supported Formats

PyTorch

• .pt files (TorchScript)
• .pth files (state_dict)
• .pkl files (pickle format)
• .tar files (archived models)

TensorFlow

• .h5 files (HDF5 format)
• SavedModel format
• .pb files (protobuf)
• Checkpoint files

Scanning Modes

• Single File: Scan individual model files
• Directory: Recursive directory scanning
• URL: Scan models from remote sources
• Batch: Process multiple files efficiently
• Continuous: Monitor directories for changes

Threat Categories

• Critical: Immediate security risk (code execution)
• High: Serious vulnerability (data access)
• Medium: Potential security issue (information disclosure)
• Low: Best practice violation
• Info: Informational findings

Integration

• CI/CD pipeline integration
• Pre-deployment security gates
• Model registry scanning
• Automated security audits
• Webhook notifications
• SIEM integration

Use Cases

• Scan models before deployment to production
• Audit third-party pre-trained models
• Verify model marketplace downloads
• Continuous security monitoring
• Compliance requirement fulfillment
• Supply chain security validation
• Research reproducibility verification

Reporting

• Detailed security reports
• CVE mapping when applicable
• Remediation recommendations
• Risk scoring
• Compliance status
• Audit trail logs

Best Practices Enforcement

• Safe serialization recommendations
• Secure model packaging
• Signature and checksums
• Version pinning
• Dependency management
• Access control guidance

API Access

• RESTful API for automation
• Command-line interface
• Python SDK
• MCP protocol integration
• Webhook callbacks

Pricing

Free and open-source. Part of the ML security ecosystem.