NLR-GAPs

v0.9.4 suspicious
5.0
Medium Risk

National Laboratory of the Rockies' (NLR's) Geospatial Analysis Pipelines (GAPs) framework

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its use of 'subprocess.run' with 'shell=True', which can lead to command injection vulnerabilities. While other risks are low, the lack of detailed author information raises concerns about transparency.

  • Use of 'subprocess.run' with 'shell=True' introduces shell risk.
  • Sparse author details suggest potential lack of transparency.
Per-check LLM notes
  • Network: No network calls were detected, which is not unusual and does not indicate immediate risk.
  • Shell: The use of 'subprocess.run' with 'shell=True' can introduce security risks such as command injection if not properly sanitized, indicating potential vulnerabilities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's details are sparse, indicating a potential lack of transparency.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • shlex.split(cmd) subprocess.call(cmd) # noqa: S603 else: msg = f"Cou
  • get piped o/e process = subprocess.Popen( # noqa: S603 cmd, stdout=subprocess.PIPE, stderr=
  • hup_cmd_fmt).format(cmd) subprocess.run(cmd, shell=True, check=True) # noqa: S602 def submit(c
  • cmd) subprocess.run(cmd, shell=True, check=True) # noqa: S602 def submit(cmd, background=
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: nlr.gov>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository NatLabRockies/gaps appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with NLR-GAPs 
Create a geospatial data analysis tool using the NLR-GAPs package. This tool will enable users to upload various geospatial datasets, perform basic analysis such as calculating distances between points, identifying areas of interest based on specific criteria, and visualizing the results on an interactive map. The application should also allow users to save their analysis and share it with others via a unique URL.

Steps:
1. Set up a Flask web application framework for the frontend and backend integration.
2. Integrate the NLR-GAPs package to handle geospatial data processing tasks.
3. Develop an interface where users can upload their geospatial datasets in common formats like GeoJSON or Shapefile.
4. Implement functions to calculate distances between points, identify areas based on user-defined criteria (e.g., area size, elevation), and perform other basic geospatial analyses.
5. Use Leaflet.js or similar library to display the analyzed data on an interactive map within the web application.
6. Add functionality to save the current state of the analysis and generate a unique URL for sharing.
7. Ensure the application is responsive and user-friendly, providing clear instructions and feedback at each step.
8. Test the application thoroughly to ensure all functionalities work as expected and handle potential errors gracefully.

Features:
- Upload geospatial datasets.
- Calculate distances between points.
- Identify areas based on user-defined criteria.
- Visualize analyzed data on an interactive map.
- Save and share the analysis via a unique URL.
- Responsive design for optimal user experience.