Geoskill: Flood Impact Assessment

• Outcome: Review
• Latest audit: Jul 31, 2026 · 12:07 PM UTC
• Version: 2.0.0

Overview

This flood-impact skill is not malicious, but it overstates what it assesses and can generate reports using synthetic population data, so users should review it carefully before operational use.

Install only if you understand that auto-download mode synthesizes population data and the tool does not actually assess buildings or cropland despite advertising those layers. Do not use its reports for emergency or operational decisions without validating the data sources, pinning dependencies, and confirming the missing assessment layers are implemented.

SkillSpector

By NVIDIA

Vulnerability Patterns

• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• MCP Tool Poisoning: Hidden Instructions, Unicode Deception, Parameter Description Injection
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access

Findings (8)

Tp4

High

• Category: MCP Tool Poisoning
• Confidence: 95% confidence
• Finding: The skill is presented as a flood impact assessment tool using real exposure layers, but the documented behavior includes auto-downloading remote data, synthesizing population data, and failing to actually assess some declared layers such as buildings and cropland. In a disaster-response context, this can mislead users into trusting incomplete or fabricated impact estimates, producing materially wrong decisions even without traditional code execution abuse.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

geoskill-data-fetcher>=0.2.0

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

• Confidence: 98% confidence
• Finding: fiona

Unpinned Dependencies

Low

• Category: Supply Chain

Content

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

• Confidence: 98% confidence
• Finding: numpy

Unpinned Dependencies

Low

• Category: Supply Chain

Content

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

• Confidence: 98% confidence
• Finding: rasterio

Unpinned Dependencies

Low

• Category: Supply Chain

Content

fiona
numpy
rasterio
shapely

• Confidence: 98% confidence
• Finding: shapely

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# Requirements for flood-impact-assessment
# Shared data-download library (Planetary Computer / NASA POWER / OSM)
geoskill-data-fetcher>=0.2.0

# Third-party dependencies used by this skill
fiona

• Confidence: 99% confidence
• Finding: geoskill-data-fetcher>=0.2.0

Known Vulnerable Dependency: fiona — 2 advisory(ies): GHSA-g4m4-9q4c-mfw6 (Fiona affected by CVE-2020-14152 related to madler-zlib); GHSA-q5fm-55c2-v6j9 (Fiona affected by CVE-2023-45853 related to MiniZip madler-zlib)

Critical

• Category: Supply Chain
• Confidence: 91% confidence
• Finding: fiona

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: 80% confidence
• Finding: numpy

VirusTotal

VirusTotal findings are pending for this skill version.

Static analysis

No suspicious patterns detected.