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Security audit

Survival Analysis (KM)

Security checks across malware telemetry and agentic risk

Overview

This is a coherent local survival-analysis tool, but users should handle clinical data and output paths carefully.

Install in an isolated Python environment, use de-identified or authorized datasets only, and write outputs to a dedicated workspace folder. Review generated plots, CSVs, and reports before sharing because they may reveal sensitive clinical information or derived statistics.

SkillSpector

By NVIDIA
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Findings (7)

Vague Triggers

Medium
Confidence
90% confidence
Finding
The trigger description is broad enough to activate on generic requests about survival analysis or biomedical statistics without clearly constraining when this skill should run. In a clinical/biomedical context, overbroad activation increases the chance the tool is invoked on sensitive health-related data or in situations where users did not explicitly intend to run a local script, which can lead to inappropriate data handling or unsafe automation.

Missing User Warnings

Medium
Confidence
88% confidence
Finding
This skill operates on clinical and biological survival datasets, which commonly contain protected or sensitive patient information, yet the description does not warn users about privacy, de-identification, or secure handling expectations. That omission makes accidental exposure more likely through unsafe input selection, saved output files, or downstream sharing of generated reports and plots.

Unpinned Dependencies

Low
Category
Supply Chain
Content
lifelines
matplotlib
numpy
pandas
Confidence
95% confidence
Finding
lifelines

Unpinned Dependencies

Low
Category
Supply Chain
Content
lifelines
matplotlib
numpy
pandas
seaborn
Confidence
95% confidence
Finding
matplotlib

Unpinned Dependencies

Low
Category
Supply Chain
Content
lifelines
matplotlib
numpy
pandas
seaborn
Confidence
98% confidence
Finding
numpy

Unpinned Dependencies

Low
Category
Supply Chain
Content
lifelines
matplotlib
numpy
pandas
seaborn
Confidence
98% confidence
Finding
pandas

Unpinned Dependencies

Low
Category
Supply Chain
Content
matplotlib
numpy
pandas
seaborn
Confidence
95% confidence
Finding
seaborn

VirusTotal

65/65 vendors flagged this skill as clean.

View on VirusTotal

Static analysis

No suspicious patterns detected.