Geoskill: Impervious Surface Mapping

• Outcome: Review
• Latest audit: Jul 31, 2026 · 12:28 PM UTC
• Version: 2.0.0

Overview

This skill mostly matches its mapping purpose, but its remote-download path can substitute synthetic demo data for real imagery, so users should review it before relying on results.

Install only if you are comfortable with a CLI that reads local geospatial files, writes outputs under the chosen output directory, and may contact Microsoft Planetary Computer when bbox/date inputs are used. Treat remote-download results carefully: check the manifest and console output to confirm the analysis used real imagery rather than synthetic demo data, and consider pinning dependencies for controlled environments.

SkillSpector

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• MCP Least Privilege: Underdeclared Capability, Wildcard Permission, Missing Permission Declaration
• MCP Tool Poisoning: Hidden Instructions, Unicode Deception, Parameter Description Injection
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands

Findings (9)

Lp3

Medium

• Category: MCP Least Privilege
• Confidence: 93% confidence
• Finding: The skill documentation describes file-reading and file-writing behavior via raster inputs, GeoJSON inputs, and output artifacts, but it does not declare permissions for those capabilities. Undeclared filesystem access is dangerous because users and orchestration systems may grant or assume a lower privilege profile than the skill actually requires, reducing transparency and weakening security controls around local data access.

Description-Behavior Mismatch

Medium

• Confidence: 90% confidence
• Finding: The script expands beyond local impervious-surface estimation by modifying import paths and optionally enabling external data discovery/download through a shared fetcher. That broader capability can trigger unanticipated network access and pull in external code/data paths, which is a real security and trust-boundary issue even if intended as a convenience feature.

Description-Behavior Mismatch

Medium

• Confidence: 95% confidence
• Finding: When auto-download fails or returns a single-band asset, the tool silently falls back to synthetic data and still produces outputs, which can mislead downstream users into believing the results correspond to the requested AOI/date. In geospatial decision workflows, this integrity failure can cause incorrect analysis, reporting, or operational decisions.

Missing User Warnings

Low

• Confidence: 88% confidence
• Finding: The skill can automatically download remote Sentinel-2 data from Microsoft Planetary Computer, but the main description does not clearly warn users that network access and external data transfer may occur. This matters because remote fetching can expose user-supplied locations and dates to a third party, introduce supply-chain and availability risks, and surprise users operating in restricted or offline environments.

Missing User Warnings

Low

• Confidence: 84% confidence
• Finding: The auto-download path sends user-supplied bbox/AOI and date-range parameters to an external service without an explicit user-facing privacy warning or confirmation. While expected for remote data retrieval, this can disclose sensitive locations or project timing information in contexts where AOIs are confidential.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

geoskill-data-fetcher>=0.2.0

# Third-party dependencies used by this skill
numpy
rasterio
shapely

• Confidence: 95% confidence
• Finding: Unpinned dependency versions make builds non-reproducible and allow future package releases to change behavior or introduce malicious or vulnerable code into the environment without review. Because this skill processes external geospatial data, dependency drift can increase supply-chain risk and make security patching harder to audit.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# Third-party dependencies used by this skill
numpy
rasterio
shapely

• Confidence: 95% confidence
• Finding: Leaving rasterio unpinned permits uncontrolled dependency resolution, which can pull in unexpected versions and transitive native-code changes. For a package that handles complex raster formats, this increases supply-chain and stability risk in deployment environments.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# Third-party dependencies used by this skill
numpy
rasterio
shapely

• Confidence: 94% confidence
• Finding: An unpinned shapely dependency creates avoidable supply-chain uncertainty and weakens reproducibility. While not inherently exploitable by itself, it expands the chance of unexpected vulnerable or incompatible versions being installed later.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# Requirements for impervious-surface-mapping
# 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 only a lower bound for geoskill-data-fetcher allows any newer release to be installed, including versions with changed capabilities, vulnerable code, or compromised supply-chain artifacts. Because this is a shared data-download component with network-facing functionality, the skill context makes version drift somewhat more security-relevant than a purely local utility library.

Static analysis

No suspicious patterns detected.