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Security audit

Geoskill: Groundwater Recharge Potential

Security checks for vulnerabilities and agentic risk

Overview

This skill is a disclosed geospatial analysis tool that reads user-provided raster data and writes local planning outputs, with no evidence of hidden data access or exfiltration.

Install in a project or virtual environment, review dependency versions if reproducibility matters, and treat the outputs as planning-screening evidence only. The tool may require local raster inputs despite documentation mentioning automatic downloads.

SkillSpector

Vulnerability Patterns
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
Findings (4)

Unpinned Dependencies

Low
Category
Supply Chain
Content
geoskill-data-fetcher>=0.2.0

# Third-party dependencies used by this skill
numpy
rasterio
scipy
Confidence
95% confidence
Finding
The dependency on numpy is unpinned, so builds may resolve to different versions over time, including versions with security defects or breaking behavior. This weakens reproducibility and increases supply-chain exposure because the deployed environment is not fixed to a reviewed version.

Unpinned Dependencies

Low
Category
Supply Chain
Content
# Third-party dependencies used by this skill
numpy
rasterio
scipy
Confidence
94% confidence
Finding
An unpinned rasterio dependency allows uncontrolled version drift, which can introduce vulnerable releases or unexpected behavior in geospatial file handling. For data-processing skills, parser-heavy libraries are particularly sensitive because malformed files may trigger defects in transitive native code.

Unpinned Dependencies

Low
Category
Supply Chain
Content
# Third-party dependencies used by this skill
numpy
rasterio
scipy
Confidence
95% confidence
Finding
Leaving scipy unpinned permits installation of arbitrary future or past compatible versions, undermining reproducibility and potentially pulling in releases with known flaws. In scientific Python stacks, this can also complicate ABI compatibility and patch management.

Unpinned Dependencies

Low
Category
Supply Chain
Content
# Requirements for groundwater-recharge-potential
# Shared data-download library (Planetary Computer / NASA POWER / OSM)
geoskill-data-fetcher>=0.2.0

# Third-party dependencies used by this skill
numpy
Confidence
96% confidence
Finding
Using geoskill-data-fetcher>=0.2.0 permits any later version, including versions not yet reviewed, which creates supply-chain risk and non-reproducible environments. Because this package has network/data acquisition capability, uncontrolled updates are somewhat more sensitive than a purely local utility library.

Static analysis

No suspicious patterns detected.