ClawHub

Heatmap Beautifier

Security checks across malware telemetry and agentic risk

Overview

This appears to be a normal data-visualization skill with disclosed local file processing and ordinary Python plotting dependencies, with hygiene issues but no artifact-backed malicious behavior.

Before installing, use it only on CSV and annotation files you intend to share with the agent, and prefer installing in an isolated Python environment with pinned or reviewed dependency versions. The scan found dependency and documentation hygiene issues, but not hidden behavior, credential handling, persistence, or exfiltration.

SkillSpector

By NVIDIA
Vulnerability Patterns
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • MCP Least PrivilegeUnderdeclared Capability, Wildcard Permission, Missing Permission Declaration
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
Findings (8)

Lp3

Medium
Category
MCP Least Privilege
Confidence
88% confidence
Finding
The skill documentation instructs the agent to read user-supplied CSV files and local annotation files, which implies file-read capability, but the skill metadata declares no permissions. This mismatch can undermine sandboxing and trust decisions because an execution framework may allow undeclared file access or fail to present the true capability surface to users and reviewers.

Intent-Code Divergence

Medium
Confidence
93% confidence
Finding
The audit artifact is internally inconsistent: it asserts that exception-handling hardening was implemented, while later sections admit the underlying script may still retain a bare except and may not catch FileNotFoundError. This can cause downstream reviewers or deployment systems to trust a security/reliability improvement that was never actually verified, allowing weak error handling to persist unnoticed in production.

Unpinned Dependencies

Low
Category
Supply Chain
Content
matplotlib
numpy
pandas
seaborn
Confidence
97% confidence
Finding
matplotlib

Unpinned Dependencies

Low
Category
Supply Chain
Content
matplotlib
numpy
pandas
seaborn
Confidence
99% confidence
Finding
numpy

Unpinned Dependencies

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

Unpinned Dependencies

Low
Category
Supply Chain
Content
matplotlib
numpy
pandas
seaborn
Confidence
96% confidence
Finding
seaborn

Known Vulnerable Dependency: numpy — 10 advisory(ies): CVE-2014-1859 (Numpy arbitrary file write via symlink attack); CVE-2021-41495 (NumPy NULL Pointer Dereference); CVE-2021-33430 (NumPy Buffer Overflow (Disputed)) +7 more

Critical
Category
Supply Chain
Confidence
84% confidence
Finding
numpy

Known Vulnerable Dependency: pandas — 1 advisory(ies): CVE-2020-13091 (** DISPUTED ** pandas through 1.0.3 can unserialize and execute commands from an)

High
Category
Supply Chain
Confidence
65% confidence
Finding
pandas

VirusTotal

65/65 vendors flagged this skill as clean.

View on VirusTotal