azure-mgmt-maintenance

v2.1.0 safe
4.0
Medium Risk

Microsoft Azure Maintenance Management Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package azure-mgmt-maintenance v2.1.0 from Microsoft Azure is deemed safe despite the use of eval(), as it includes a security bypass comment (nosec). There are no signs of network, shell, credential, or metadata risks.

  • Use of eval() with nosec
  • No network or shell execution detected
  • No credential harvesting patterns found
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package that does not require external API interactions.
  • Shell: No shell execution patterns detected, which is expected for a legitimate Python package.
  • Obfuscation: The use of eval() function with no security bypass (nosec) suggests potential for code injection and obfuscation.
  • Credentials: No patterns indicative of credential harvesting were found.
  • Metadata: The author has only one package on PyPI, which may indicate a new or less active account, but no other red flags are present.

📦 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 (6564 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
  • 65 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)

  • _unicode(data) return eval(data_type)(data) # nosec @classmethod def serializ
  • _unicode(attr) return eval(data_type)(attr) # nosec @staticmethod def deseria
  • __path__ = __import__("pkgutil").extend_path(__path__, __name__) __path__ = __import__("pkg
  • path__, __name__) __path__ = __import__("pkgutil").extend_path(__path__, __name__) # coding=utf-8 # ---------
✓ 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

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Azure/azure-sdk-for-python appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Microsoft Corporation" 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-maintenance 
Create a Python-based command-line tool that leverages the 'azure-mgmt-maintenance' library to manage maintenance scopes and schedules within Azure. This tool should allow users to perform the following operations:

1. List all available maintenance scopes in a given Azure subscription.
2. Create new maintenance scopes based on user-defined criteria such as resource ID, scope type, and tags.
3. Update existing maintenance scopes to modify their properties or associated resources.
4. Delete maintenance scopes when they are no longer needed.
5. Schedule maintenance activities for specific time periods, ensuring minimal impact on production workloads.
6. Retrieve details of scheduled maintenance activities, including start times, duration, and affected resources.
7. Cancel pending maintenance activities if necessary.

The tool should authenticate using Azure Active Directory credentials provided via environment variables or interactive login. It should also support configuration through a YAML file for ease of use and automation.

Incorporate error handling and logging mechanisms to ensure robustness and provide meaningful feedback to the user. Additionally, include documentation and examples in the form of Jupyter notebooks or README files to facilitate easy adoption by other developers.

💬 Discussion Feed

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