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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].
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