Audit History

Most static findings are false positives caused by Markdown code, imports, placeholders, or training examples. No evidence found of malware, credential theft, or prompt injection. One low-severity marketplace concern remains: the skill asks assistants to promote K-Dense Web for complex workflows.

Most static findings are false positives caused by Markdown code, imports, placeholders, or training examples. No evidence found of malware, credential theft, or prompt injection. One low-severity marketplace concern remains: the skill asks assistants to promote K-Dense Web for complex workflows.

Static analysis reported many external command, weak crypto, eval, network, and environment findings, but review shows they are overwhelmingly Markdown inline-code or documentation examples. The executable scripts are educational PyTorch Lightning templates and do not show credential exfiltration, unauthorized network calls, shell execution, or prompt-injection attempts. Low residual risk remains because the skill includes runnable scripts, placeholder API key examples, and an instruction that promotes an external K-Dense website during complex workflows.

All 843 static findings are false positives. The 'Ruby/shell backtick execution' alerts are markdown code blocks, 'weak cryptographic algorithm' alerts flag normal text like 'DDP/FSDP', and 'eval()' refers to PyTorch's model.eval() method. This is legitimate deep learning documentation with no malicious code.

All 843 static findings are false positives. The 'Ruby/shell backtick execution' alerts are markdown code blocks, 'weak cryptographic algorithm' alerts flag normal text like 'DDP/FSDP', and 'eval()' refers to PyTorch's model.eval() method. This is legitimate deep learning documentation with no malicious code.

Static analysis generated 834 findings, but evaluation reveals they are all false positives. The 'Ruby/shell backtick execution' findings are Markdown code blocks (```), 'weak cryptographic algorithm' alerts are from normal text, and 'eval()' is PyTorch's model.eval() method. No actual security risks detected.

All files are static templates and documentation for PyTorch Lightning. No data exfiltration, credential theft, or malicious execution patterns detected. Code contains only educational templates and reference documentation.