Audit-Verlauf

All 777 static findings are false positives. The detected 'eval()' calls are legitimate PyTorch model.eval() methods for evaluation mode, not dynamic code execution. The 'external_commands' findings are markdown code blocks using backtick formatting. The 'weak cryptographic algorithm' flags are triggered by documentation mentioning algorithm names. The 'certificate/key files' flags misidentify error messages referencing TEMPLATES.keys(). No actual security threats identified in this legitimate PyTorch Geometric ML skill.

All 777 static findings are false positives. The detected 'eval()' calls are legitimate PyTorch model.eval() methods for evaluation mode, not dynamic code execution. The 'external_commands' findings are markdown code blocks using backtick formatting. The 'weak cryptographic algorithm' flags are triggered by documentation mentioning algorithm names. The 'certificate/key files' flags misidentify error messages referencing TEMPLATES.keys(). No actual security threats identified in this legitimate PyTorch Geometric ML skill.

The static analysis findings are false positives. The detected 'eval()' calls are legitimate PyTorch model.eval() methods for setting models to evaluation mode, not dynamic code execution. The 'external_commands' findings are markdown code blocks in documentation. No actual security threats were identified.

This skill contains documentation and utility Python scripts for PyTorch Geometric. The scripts perform model benchmarking, visualization, and template generation without credential access, environment harvesting, or suspicious network patterns. Dataset downloads use standard PyG APIs to public benchmarks only.