Geoskill: Forest Fire Burn Severity

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

Overview

This skill needs Review because its automatic download mode can produce burn-severity reports from unsuitable or heuristically assigned imagery, though I found no credential theft, destructive action, or hidden persistence.

Install only with care. Prefer supplying verified local pre-fire and post-fire NIR/SWIR GeoTIFFs. Treat bbox/date auto-download outputs as demo or exploratory unless the downloaded bands, dates, alignment, and scene ordering are independently checked. Be aware that AOI/bbox and date-range queries may be sent to external imagery services and cached locally.

SkillSpector

By NVIDIA

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep
• MCP Tool Poisoning: Hidden Instructions, Unicode Deception, Parameter Description Injection
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands

Findings (9)

Tp4

High

• Category: MCP Tool Poisoning
• Confidence: 91% confidence
• Finding: The documented purpose is local burn-severity computation from specific pre/post NIR and SWIR inputs, but the skill also appears to fetch remote assets, use fallback imagery types, and infer band/time assignments heuristically. That mismatch can cause users to unknowingly permit network access and produce scientifically invalid or misleading severity outputs, which is risky in an environmental assessment context.

Context-Inappropriate Capability

Medium

• Confidence: 91% confidence
• Finding: The requirements include a shared data-download library even though the skill is described as performing local burn-severity computation from provided imagery. This expands the attack surface by introducing code capable of network/data-access behavior not clearly required by the stated purpose, which could enable unnecessary external access or supply-chain risk if the dependency is compromised.

Intent-Code Divergence

Low

• Confidence: 83% confidence
• Finding: The comments indicate external data-download behavior that is not reflected in the manifest description, creating a transparency gap about what the skill may do. Hidden or undocumented network-related capability is a security concern because reviewers and operators may permit the skill under incorrect assumptions about its behavior.

Description-Behavior Mismatch

High

• Confidence: 98% confidence
• Finding: The auto-download workflow fetches generic visual/RGB or unrelated band assets and then assigns them to the pre/post NIR and SWIR inputs used for dNBR. That produces scientifically invalid burn-severity outputs while the report and manifest still present the result as if it came from proper pre/post-fire NIR/SWIR analysis, which is an integrity issue that can mislead downstream decisions.

Description-Behavior Mismatch

High

• Confidence: 99% confidence
• Finding: This code assigns a single downloaded file to all four inputs, making pre-fire and post-fire imagery identical and collapsing the dNBR calculation into meaningless output. Because the skill still emits a formal burn-severity report and manifest, users may trust incorrect results for situational awareness or resource allocation.

Missing User Warnings

Medium

• Confidence: 88% confidence
• Finding: The documentation states that the skill may automatically download data from an external service without clearly warning the user about network access, external requests, caching, or possible transmission of AOI/bounding-box information. Undisclosed network behavior is a real security and privacy concern because users may run the skill assuming it only processes local files.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

geoskill-data-fetcher>=0.2.0

# Third-party dependencies used by this skill
numpy
rasterio

• Confidence: 95% confidence
• Finding: numpy

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# Third-party dependencies used by this skill
numpy
rasterio

• Confidence: 95% confidence
• Finding: rasterio

Unpinned Dependencies

Low

• Category: Supply Chain

Content

# Requirements for forest-fire-burn-severity
# Shared data-download library (Planetary Computer / NASA POWER / OSM)
geoskill-data-fetcher>=0.2.0

# Third-party dependencies used by this skill
numpy

• Confidence: 90% confidence
• Finding: geoskill-data-fetcher>=0.2.0

VirusTotal

VirusTotal findings are pending for this skill version.

Static analysis

No suspicious patterns detected.