arm-adapt

v0.1.3 suspicious
5.0
Medium Risk

Real-Time Analysis, Tracking, and Adaptive Radar Sampling of Convective Storms

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to its handling of AWS credentials and potential for shell command execution. While there is no direct evidence of malicious intent, the sparse metadata and use of sensitive credentials raise concern.

  • Credential risk due to AWS credential access
  • Potential shell risk from command execution
Per-check LLM notes
  • Network: No network calls detected, which is not necessarily suspicious but could indicate limited functionality.
  • Shell: Detection of shell execution suggests potential for executing arbitrary commands, which may pose a risk depending on how and when it's used.
  • Obfuscation: No obfuscation patterns were detected in the package.
  • Credentials: The package appears to be accessing AWS credentials and an S3 bucket which could indicate potential unauthorized access or credential harvesting activities.
  • Metadata: The author's information is sparse and the account seems new or inactive, which raises some concerns but not enough to conclusively label it as malicious.

📦 Package Quality Overall: Medium (6.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://arm-doe.github.io/Adapt/
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4443 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 213 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in ARM-DOE/Adapt
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • try: self._proc = subprocess.Popen( cmd, stdout=subprocess.PIPE
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • onment variables or ~/.aws/credentials. The S3 bucket is public and requires no auth, but
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: anl.gov>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ARM-DOE/Adapt 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 arm-adapt 
Create a real-time storm tracking application using the 'arm-adapt' Python package. This application will allow users to monitor convective storms in their area and receive adaptive radar sampling data directly through a user-friendly interface. Here’s how you can structure your project:

1. **Project Setup**: Start by setting up a virtual environment and installing the 'arm-adapt' package along with other necessary libraries such as Flask for the web interface and Matplotlib for visualizing the radar data.

2. **Data Retrieval**: Use 'arm-adapt' to fetch real-time radar data from available sources. Ensure the application supports adaptive sampling based on storm intensity and proximity to populated areas.

3. **Storm Detection**: Implement algorithms within the application that utilize 'arm-adapt' to detect and classify different types of convective storms (e.g., thunderstorms, tornadoes). Display these classifications on the map.

4. **User Interface**: Develop a simple yet intuitive web interface using Flask where users can select geographic locations and view live storm tracking data. Include interactive maps that update in real-time based on the radar data processed by 'arm-adapt'.

5. **Notifications**: Integrate notification systems that alert users about severe weather conditions detected by the application. Notifications can be sent via email or SMS based on user preferences.

6. **Data Visualization**: Utilize Matplotlib to create dynamic visualizations of the radar data. Show storm paths, intensities, and other relevant metrics in a clear and understandable format.

7. **Adaptive Sampling**: Implement adaptive radar sampling techniques provided by 'arm-adapt' to focus more resources on areas with higher storm activity, improving the accuracy and efficiency of the data collected.

8. **Testing and Deployment**: Thoroughly test the application for functionality, performance, and usability. Once satisfied, deploy the application on a cloud platform like AWS or Heroku so it can be accessed by users around the world.

This project aims to provide a powerful yet accessible tool for individuals and communities to stay informed about potentially dangerous weather conditions, leveraging the advanced capabilities of the 'arm-adapt' package.

💬 Discussion Feed

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