Vulnerability Assessment Report: PyTorch v2.1.0

Report Date: May 2, 2025
Assessment ID: def456

Executive Summary

PyTorch v2.1.0 demonstrates strong security practices with a few areas for improvement. The library has low overall risk (2.7/10) with particularly strong maintenance and licensing practices. Primary concerns are in dependency management and a few pending security issues.

Risk Score Breakdown

Risk Domain	Score	Risk Level
License Validation	1.8/10	Low
Security Assessment	3.2/10	Low-Medium
Maintenance Health	2.0/10	Low
Dependency Management	2.5/10	Low
Regulatory Compliance	4.1/10	Medium

1. License Validation

Score: 1.8/10 (Low Risk)

PyTorch is licensed under the BSD-3-Clause license, which is permissive and compatible with most commercial and open-source applications. The license is properly applied across all repository components with clear attribution guidelines.

Key Findings:

License type: BSD-3-Clause
Patent protection: Present and adequate
License compliance: High (proper notices in all files)
License compatibility: High with most ecosystems

Recommendations:

Continue maintaining clear license documentation
Consider providing guidance on license compliance for extensions and derivatives

2. Security Assessment

Score: 3.2/10 (Low-Medium Risk)

PyTorch exhibits good security practices with a few areas of concern. The security team is responsive, and vulnerabilities are addressed promptly.

Identified Vulnerabilities:

CVE-2025-7712: Memory corruption in C++ extensions (Patched)
CVE-2025-7713: Incorrect validation in serialization routines (Patched)

Security Controls:

Input validation: Well-implemented
Memory safety controls: Strong
Code signing: Present
Dependency validation: Present but not comprehensive

Recommendations:

Enhance serialization validation for untrusted inputs
Implement more rigorous fuzzing in the CI pipeline
Further improve CUDA extension memory safety checks

3. Maintenance Health

Score: 2.0/10 (Low Risk)

PyTorch demonstrates excellent maintenance practices with a large active community and regular release cadence.

Key Metrics:

156 active contributors in the last 6 months
Average PR review time: 2.5 days
Release frequency: Every 4-6 weeks
Test coverage: 92%
Issue response time: Medium (3.2 days average)

Recommendations:

Continue the current maintenance practices
Consider improving documentation for new contributors

4. Dependency Management

Score: 2.5/10 (Low Risk)

PyTorch has a well-managed dependency tree with minimal vulnerable components.

Key Findings:

Direct dependencies: 18
Transitive dependencies: 42
Vulnerable dependencies: 1 (low severity)
SBOM available: Yes
Dependency update process: Well-documented

Recommendations:

Update the identified vulnerable dependency
Implement automated dependency scanning in nightly builds

5. Regulatory Compliance

Score: 4.1/10 (Medium Risk)

PyTorch provides basic documentation for regulatory considerations but could improve its guidance for compliance-sensitive deployments.

Key Compliance Areas:

AI/ML regulatory frameworks: Basic documentation
Data protection features: Limited
Model transparency tools: Good implementation
Audit capabilities: Limited

Recommendations:

Enhance documentation specific to EU AI Act compliance
Provide better guidance on implementing data minimization
Develop tools for model explanations in compliance-sensitive contexts

Appendix: Assessment Methodology

This assessment was conducted using the LibVulnWatch methodology, which includes:

Static code analysis
Dependency scanning
License validation
Maintenance metrics analysis
Expert review of security controls

For questions about this report, contact [email protected].