Geoskill: Landslide Susceptibility Assessment

• Outcome: Review
• Latest audit: Jul 31, 2026 · 1:25 PM UTC
• Version: 3.0.0

Overview

Review is recommended because the skill can produce landslide-analysis artifacts that are not backed by the real geospatial factors its description implies.

Use this only as a demo or prototype unless the publisher adds real factor-raster inputs, fails closed when required data is missing, clearly labels synthetic outputs, and wires downloaded DEM data into the model. Be aware that bbox/AOI runs may contact Microsoft Planetary Computer, and prefer pinned dependency versions for reproducible installs.

SkillSpector

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 Least Privilege: Underdeclared Capability, Wildcard Permission, Missing Permission Declaration
• MCP Tool Poisoning: Hidden Instructions, Unicode Deception, Parameter Description Injection

Findings (14)

Lp3

Medium

• Category: MCP Least Privilege
• Confidence: 90% confidence
• Finding: The skill documentation exposes file read and file write capabilities through its CLI workflow, but no permissions are declared. This creates a trust and containment problem: a caller or hosting platform may treat the skill as low-privilege while it can still access local inputs and write outputs, increasing the risk of unintended data access or overwrite in environments that rely on declared permissions for policy enforcement.

Tp4

High

• Category: MCP Tool Poisoning
• Confidence: 95% confidence
• Finding: The documented behavior materially differs from the stated purpose: the skill can auto-download remote DEM data, operate in synthetic/demo mode, and apparently does not truly integrate the full set of claimed geospatial factors. This is dangerous because users may make planning or safety-related decisions based on fabricated or incomplete inputs while believing the analysis reflects real terrain, geology, rainfall, land cover, roads, and historical landslide evidence.

Description-Behavior Mismatch

High

• Confidence: 99% confidence
• Finding: In file-based mode the pipeline claims to assess susceptibility using terrain, geology, rainfall, land cover, roads, and historical landslide data, but it actually generates synthetic factor rasters and trains on those synthetic features. This can mislead users into treating outputs as evidence-based geospatial analysis when they are not, creating a serious integrity and decision-support risk in planning or safety contexts.

Description-Behavior Mismatch

High

• Confidence: 99% confidence
• Finding: For non-synthetic runs, the code returns zero-valued placeholder susceptibility, zone, and uncertainty maps instead of producing a real modeled surface. Because the skill still writes normal-looking output artifacts, users may incorrectly trust fabricated results for hazard zoning decisions.

Context-Inappropriate Capability

Medium

• Confidence: 87% confidence
• Finding: The script can automatically contact Microsoft Planetary Computer and download external data when bbox or AOI arguments are supplied, expanding the skill's behavior beyond local processing. This introduces unannounced network egress, dependency on remote services, and possible exposure of sensitive AOI information or execution context metadata.

Intent-Code Divergence

Medium

• Confidence: 90% confidence
• Finding: The inline documentation says bbox-driven workflows attach real elevation data, but the downloaded DEM is not consumed by the modeling pipeline. This is dangerous because it creates false provenance and may cause users or downstream systems to believe outputs were informed by real terrain data when they were not.

Missing User Warnings

Medium

• Confidence: 84% confidence
• Finding: The auto-download path performs external network fetches without a clear upfront warning or explicit consent step, which is problematic in security-sensitive or privacy-sensitive environments. Users may unknowingly disclose AOI bounds or trigger network activity in restricted runtimes.

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: 96% confidence
• Finding: The dependency 'fiona' is unpinned, so builds may resolve to different versions over time, including newly introduced vulnerable or incompatible releases. In a geospatial processing skill that ingests external files and data sources, dependency drift increases supply-chain risk and makes security review and reproducibility harder.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

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

• Confidence: 96% confidence
• Finding: The dependency 'numpy' is unpinned, allowing uncontrolled upgrades or dependency resolution changes that can introduce vulnerable versions or break assumptions in the skill. Because NumPy is a foundational native-extension package, version drift can have broad stability and security consequences across the environment.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

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

• Confidence: 96% confidence
• Finding: The dependency 'rasterio' is unpinned, which weakens reproducibility and can pull in unexpected releases with security defects in raster parsing or native libraries. This is more relevant here because the skill likely handles untrusted geospatial raster inputs, where parser bugs can be security-sensitive.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

fiona
numpy
rasterio
shapely

• Confidence: 95% confidence
• Finding: The dependency 'shapely' is unpinned, making installs non-deterministic and increasing exposure to vulnerable or incompatible releases. In spatial-analysis code, geometry libraries are frequently exercised on external data, so unmanaged upgrades can expand attack surface or destabilize processing.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

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

# Third-party dependencies used by this skill
fiona

• Confidence: 94% confidence
• Finding: Using 'geoskill-data-fetcher>=0.2.0' leaves the upper bound open, so future installs may consume newer, unreviewed versions of a shared data-download library. Because this package likely touches remote data sources and network I/O, supply-chain compromise or unsafe changes could directly affect the skill's behavior.

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: 92% confidence
• Finding: The requirements file includes 'fiona' without constraining it to a non-vulnerable release, while the scanner reports known advisories affecting Fiona-related bundled libraries such as zlib/minizip. In this skill context, Fiona is likely used to parse external vector geospatial files, which makes vulnerable archive/parsing components more dangerous because malformed inputs can trigger denial of service or memory-corruption paths in native code.

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: The requirements file includes 'numpy' without a safe version constraint, and the scanner reports multiple historical advisories. While some NumPy CVEs are environment-specific or disputed, leaving NumPy unpinned means vulnerable builds could be installed, and as a compiled foundational library this can affect the broader process through crashes or unsafe memory handling.

Static analysis

No suspicious patterns detected.