Historial de auditorías
exploratory-data-analysis - 4 auditorías
Versión de auditoría 4
Más reciente SeguroJan 17, 2026, 07:10 AM
After thorough evaluation of 1077 static findings, all are false positives. The scanner misinterpreted Markdown code formatting (backticks) as shell commands, bioinformatics format names (SAM) as Windows credentials, and documentation references to file format specifications as weak cryptography. The skill is a legitimate scientific data analysis tool that only reads data files and writes markdown reports. No network access, no command execution, and no sensitive data handling were found.
Factores de riesgo
📁 Acceso al sistema de archivos (1)
Versión de auditoría 3
SeguroJan 17, 2026, 07:10 AM
After thorough evaluation of 1077 static findings, all are false positives. The scanner misinterpreted Markdown code formatting (backticks) as shell commands, bioinformatics format names (SAM) as Windows credentials, and documentation references to file format specifications as weak cryptography. The skill is a legitimate scientific data analysis tool that only reads data files and writes markdown reports. No network access, no command execution, and no sensitive data handling were found.
Factores de riesgo
📁 Acceso al sistema de archivos (1)
Versión de auditoría 2
SeguroJan 12, 2026, 04:42 PM
After thorough evaluation of 1063 static findings, all are false positives. The scanner misinterpreted Markdown code formatting (backticks) as shell commands, bioinformatics format names (SAM) as Windows credentials, and documentation references to hashing/checksums as weak cryptography. The skill is a legitimate scientific data analysis tool with no network access, no command execution, and no sensitive data handling.
Factores de riesgo
⚙️ Comandos externos (710)
📁 Acceso al sistema de archivos (10)
Versión de auditoría 1
Riesgo bajoJan 4, 2026, 04:21 PM
This is a legitimate scientific data analysis skill with standard file I/O capabilities. The Python script reads user-provided data files, performs statistical analysis using standard scientific libraries (numpy, pandas, Biopython, Pillow), and generates markdown reports. No network calls, no credential access, no code execution vulnerabilities. All capabilities are necessary for the stated purpose of exploratory data analysis.