azure-planetarycomputer

v1.0.0 safe
4.0
Medium Risk

Microsoft Corporation Azure Planetarycomputer Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with low risks across most categories. While there is some concern regarding incomplete author metadata and use of base64 encoding, these alone do not strongly indicate malicious intent.

  • Incomplete author metadata
  • Use of base64 encoding
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no direct system command execution by the package.
  • Obfuscation: The code uses base64 decoding which could be a sign of obfuscation but might also be part of normal functionality for handling serialized data.
  • Credentials: No clear patterns indicative of credential harvesting were detected.
  • Metadata: The author's details are incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 21 test file(s) found

  • Test runner config found: conftest.py
  • 21 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (26213 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 211 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 35 unique contributor(s) across 100 commits in Azure/azure-sdk-for-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • return attr return bytes(base64.b64decode(attr)) def _deserialize_bytes_base64(attr): if isinsta
  • ce("_", "/") return bytes(base64.b64decode(encoded)) def _deserialize_duration(attr): if isinstan
  • PNG image png_bytes = base64.b64decode( "iVBORw0KGgoAAAANSUhEUgAAABAAAAAJCAIAAAC0SDtlAA
  • __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore # coding=u
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: microsoft.com> license-expression: mit

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Azure/azure-sdk-for-python 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 azure-planetarycomputer 
Create a Python-based mini-application that leverages the 'azure-planetarycomputer' library to process satellite imagery data. This application will serve as a tool for environmental scientists and researchers to analyze changes in land use over time. The application should include the following functionalities:

1. User Authentication: Implement a secure way for users to authenticate themselves using their Azure credentials.
2. Data Retrieval: Allow users to specify a geographic area of interest and retrieve relevant satellite imagery data from the Azure Planetary Computer.
3. Image Processing: Integrate image processing capabilities to perform tasks such as contrast enhancement, noise reduction, and false color rendering on the retrieved images.
4. Change Detection: Develop algorithms that can compare two sets of images taken at different times to identify changes in land use, vegetation health, water bodies, etc.
5. Visualization: Provide an interactive visualization feature where users can view processed images side-by-side or overlaid on a map for better comparison.
6. Export Options: Enable users to export their analysis results in various formats like PDF reports, CSV files containing key metrics, or even shareable web links.
7. Documentation: Ensure comprehensive documentation is provided explaining how each feature works and any prerequisites needed to run the application successfully.

The 'azure-planetarycomputer' package will be utilized throughout the project for authenticating requests to the Azure Planetary Computer, fetching datasets, and possibly for other advanced operations related to handling large-scale geospatial data. Your task is to design and implement this application from scratch, ensuring it is user-friendly and efficient.

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