# 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

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## 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 assessment@libvulnwatch.org.

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