azure-mgmt-loganalytics

v13.1.1 safe
3.0
Low Risk

Microsoft Azure Loganalytics Management Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package is considered safe based on the low risk scores across all categories, indicating no signs of malicious behavior or supply-chain attack.

  • No network calls or shell executions detected.
  • Low obfuscation and credential risks.
Per-check LLM notes
  • Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity; the package may be designed to work offline.
  • Shell: No shell execution patterns detected, aligning with expectations for a legitimate package that does not require administrative privileges.
  • Obfuscation: The observed pattern is likely for package extension and not malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author information is incomplete, suggesting a potentially less established or monitored package.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

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

Some documentation present

  • Detailed PyPI description (20424 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

  • 351 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 4.0

Found 2 obfuscation pattern(s)

  • __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore __path__ =
  • ) # type: ignore __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-mgmt-loganalytics 
Create a Python-based utility that leverages the 'azure-mgmt-loganalytics' package to manage and query data from Azure Log Analytics workspaces. This utility will serve as a powerful tool for monitoring and analyzing logs across multiple Azure resources. Here’s a detailed breakdown of the project requirements and functionalities:

1. **Authentication Setup**: Begin by setting up authentication to access your Azure resources. Use environment variables to securely store your Azure credentials.
2. **Workspace Management**: Implement functions to create, update, and delete Log Analytics workspaces within your Azure subscription. Ensure that you handle exceptions gracefully and provide informative error messages.
3. **Data Querying**: Develop a feature to run queries against the Log Analytics workspace(s). Users should be able to specify the query string and receive results in a structured format (e.g., JSON).
4. **Query Scheduling**: Allow users to schedule queries to run at specific intervals (daily, weekly, etc.). Store scheduled queries and their results persistently using a local SQLite database.
5. **Notification System**: Integrate a simple notification system that alerts users via email when a scheduled query returns unexpected results or errors.
6. **User Interface**: Design a command-line interface (CLI) for easy interaction with the utility. Include options for adding, modifying, and deleting scheduled queries, as well as running ad-hoc queries.
7. **Documentation and Testing**: Provide comprehensive documentation on how to set up and use the utility. Write unit tests to ensure each component works as expected.

This project will demonstrate your ability to work with Azure services through Python, manage cloud resources programmatically, and build useful utilities that enhance productivity and operational efficiency.

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