Audit-Verlauf

All 89 static findings are false positives caused by Python import formatting, machine learning terminology, documented local caching, Markdown backticks, and official documentation links. No prompt injection, credential access, exfiltration, unsafe dynamic execution, or hostile intent was found.

All 89 static alerts were reviewed and adjudicated as false positives. The alerts come from Markdown examples, static scikit-learn imports, local model and cache examples, official documentation links, and explicit local demo commands. No prompt injection, secret exfiltration, hidden network calls, or malicious intent was found.

Static findings were reviewed in context; the import, grid search, temp cache, URL, and Markdown backtick matches are false positives from normal scikit-learn documentation and examples. No prompt injection or data-exfiltration intent was found. The remaining content-level issue is unsafe pickle loading guidance without a trusted-source warning.

AI review dismisses the critical heuristic and most static findings as false positives caused by markdown code fences, normal Python imports, ML terminology, and official documentation links. No prompt injection evidence was found in targeted review. The remaining real concerns are low-risk local artifact writes and serialized model loading examples that require trusted inputs.

All 566 static findings are FALSE POSITIVES. The static analyzer misidentified markdown documentation syntax (backticks for code formatting) as shell commands, Python import examples as dynamic imports, and 'PC2' (Principal Component 2) as C2 command-and-control keywords. This is a pure documentation/reference skill containing legitimate scikit-learn ML examples with no network calls, credential access, or file exfiltration capabilities.

All 566 static findings are FALSE POSITIVES. The static analyzer misidentified markdown documentation syntax (backticks for code formatting) as shell commands, Python import examples as dynamic imports, and 'PC2' (Principal Component 2) as C2 command-and-control keywords. This is a pure documentation/reference skill containing legitimate scikit-learn ML examples with no network calls, credential access, or file exfiltration capabilities.

This is a pure documentation and reference skill containing legitimate scikit-learn machine learning examples. The two Python scripts are standard ML workflows for classification and clustering. No network calls, no credential access, no shell commands, no file exfiltration capabilities. All capabilities match the stated purpose of providing ML guidance.

This is a pure documentation and reference skill containing legitimate scikit-learn machine learning examples. The two Python scripts are standard ML workflows for classification and clustering. No network calls, no credential access, no shell commands, no file exfiltration capabilities. All capabilities match the stated purpose of providing ML guidance.

This is a pure documentation and reference skill containing legitimate scikit-learn machine learning examples. The two Python scripts are standard ML workflows for classification and clustering. No network calls, no credential access, no shell commands, no file exfiltration capabilities. All capabilities match the stated purpose of providing ML guidance.