azureml-registry-tools

v0.1.0a57 safe
4.0
Medium Risk

AzureML Registry tools and CLI

🤖 AI Analysis

Final verdict: SAFE

The package exhibits moderate risks primarily due to potential command injection via shell=True usage and some signs of low maintenance. However, these do not strongly indicate malicious intent or a supply-chain attack.

  • moderate shell risk
  • low maintenance metadata
Per-check LLM notes
  • Network: The network calls are likely for API interactions, which is typical for Azure ML registry operations.
  • Shell: Use of shell=True can introduce security risks like command injection, especially if user inputs are involved.
  • Obfuscation: The observed pattern is commonly used for extending package paths and is not inherently malicious.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The package shows some signs of low maintenance but does not exhibit clear malicious indicators.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 110 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • p('/') self.session = requests.Session() if api_key: self.session.headers.updat
  • m_metadata}") response = requests.put(url, headers=headers, json=system_metadata) if response
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • ""__init.py__.""" __path__ = __import__('pkgutil').extend_path(__path__, __name__) # ------------------------
  • ""__init__.py.""" __path__ = __import__('pkgutil').extend_path(__path__, __name__) # ------------------------
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • m compatibility.""" # Use shell=True on Windows, shell=False on Unix-like systems use_shell
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Microsoft Corp" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with azureml-registry-tools 
Create a Python-based mini-application that serves as a streamlined interface for managing Azure Machine Learning (AML) registries using the 'azureml-registry-tools' package. This tool will allow users to perform various operations on AML models and assets stored in their registry without needing to manually interact with Azure's portal or command line interface. Here’s a detailed breakdown of the project scope and features:

1. **User Authentication**: Implement a secure method for users to authenticate with their Azure account, ensuring that only authorized individuals can access and manage the AML registry.
2. **Registry Management**: Allow users to create, update, and delete AML registries. Additionally, provide functionality to list all available registries associated with their Azure subscription.
3. **Model Operations**: Enable the ability to upload new models into the registry, download existing models, and delete models from the registry. Ensure that metadata such as model name, version, description, and tags are properly managed.
4. **Version Control**: Integrate version control features so that each time a model is uploaded, it gets assigned a unique version number, and previous versions are preserved for historical tracking.
5. **Search Functionality**: Implement a search feature that allows users to find specific models based on criteria like name, version, or tags.
6. **Notifications and Logging**: Add logging capabilities to track all operations performed via the tool, and implement email notifications for critical events such as successful uploads/downloads or errors during operations.
7. **Interactive CLI**: Develop an interactive command-line interface (CLI) that simplifies user interaction with the tool. Commands should be intuitive and well-documented.
8. **Documentation and Help**: Provide comprehensive documentation and a help section within the CLI that explains how to use each command effectively.

The 'azureml-registry-tools' package will be heavily utilized throughout the development process, particularly for its CLI functionalities which will form the backbone of our application's backend logic. It will handle the low-level interactions with Azure's ML services, allowing us to focus on building a user-friendly interface and additional value-added features.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!