azure-mgmt-operationsmanagement

v1.0.1 safe
4.0
Medium Risk

Microsoft Azure Operationsmanagement Management Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate based on the low scores for network and shell risks, and no evidence of credential harvesting. However, incomplete author metadata and moderate obfuscation raise minor concerns.

  • Incomplete author metadata
  • Moderate obfuscation techniques
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package that doesn't require real-time interaction with Azure services during installation.
  • Shell: No shell execution patterns detected, aligning with expectations for a legitimate Python package.
  • Obfuscation: The observed pattern is a common technique used for extending module search paths and is generally not indicative of malicious activity.
  • Credentials: No suspicious patterns related to credential harvesting were identified.
  • Metadata: The author information is incomplete, which may indicate a lack of transparency.

📦 Package Quality Overall: Medium (5.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4922 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 37 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-operationsmanagement 
Your task is to develop a Python-based utility named 'AzureLogWatcher' that leverages the 'azure-mgmt-operationsmanagement' package to monitor and manage logs from Azure Log Analytics workspaces. This utility will provide a user-friendly interface to query logs, set up alerts based on specific criteria, and visualize log data trends over time. Here’s a detailed breakdown of what your utility should accomplish:

1. **Setup and Authentication:** Your application should authenticate using Azure Active Directory (AAD) credentials to gain access to the Azure Management API. Ensure you guide users through setting up their AAD credentials securely.

2. **Query Logs:** Implement a feature that allows users to input custom Kusto Query Language (KQL) queries to retrieve logs from specified Azure Log Analytics workspaces. The output should be presented in a readable format, such as tables or graphs.

3. **Alert Setup:** Enable users to define alert rules based on their queries. These rules should trigger notifications (via email or SMS) when certain conditions are met in the log data. For example, if there are more than 50 failed login attempts in an hour, send an alert.

4. **Trend Analysis:** Provide a visual representation of log data trends over a selected period. Users should be able to choose metrics such as failed login attempts, successful login counts, etc., and view these trends in graphical form.

5. **User Interface:** Develop a simple command-line interface (CLI) for ease of use. Consider adding options for advanced users who might want to script interactions with your utility.

6. **Documentation:** Include comprehensive documentation that explains how to install dependencies, authenticate, run queries, set up alerts, and interpret results.

The 'azure-mgmt-operationsmanagement' package is central to this utility, providing the necessary APIs to interact with Azure Operations Management services. Use its capabilities to manage operations management resources, including Log Analytics workspaces, and to execute actions like querying logs and managing alerts. Remember to handle errors gracefully and ensure all interactions are secure and efficient.

💬 Discussion Feed

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