azure-ai-discovery

v1.0.0b1 suspicious
4.0
Medium Risk

Microsoft Corporation Azure AI Discovery Client Library for Python

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network and shell activities but has a higher metadata risk due to incomplete author information and a potentially new or inactive account.

  • Low network risk
  • Low shell risk
  • High metadata risk
Per-check LLM notes
  • Network: No network calls suggest the package does not perform external communications, which is typical for many packages focusing solely on local functionality.
  • Shell: No shell execution patterns indicate that the package does not execute system commands, reducing the risk of unauthorized system modifications or data exfiltration.
  • Metadata: The package is newly released with incomplete author information and a new or inactive account, raising suspicion.

📦 Package Quality Overall: Medium (6.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (10346 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

  • 275 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
  • __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 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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-ai-discovery 
Create a Python-based mini-application named 'AzureAIExplorer' that leverages the Azure AI Discovery service to help users explore and analyze a vast repository of documents and web content. This application should allow users to input a query and receive relevant results from various sources such as articles, research papers, news items, etc., categorized by relevance and source type. Additionally, it should provide functionality to filter results based on date range, document type, and language preference.

The application will consist of several key components:
1. User Interface: A simple yet effective command-line interface (CLI) or a basic web interface using Flask, allowing users to enter their search queries and apply filters.
2. Query Processing: Utilize the 'azure-ai-discovery' library to process user inputs and generate search queries. Ensure that the queries are optimized for retrieving the most relevant information.
3. Result Display: Present the retrieved results in a structured format, including snippets of the content, source URL, publication date, and document type. Implement pagination if necessary to handle large result sets.
4. Filtering Options: Provide options for users to filter results based on specific criteria such as date range (e.g., last month, last year), document type (e.g., PDF, HTML), and language.
5. Error Handling: Include robust error handling mechanisms to manage scenarios where the service might not return expected results or encounters unexpected issues.
6. Documentation: Prepare comprehensive documentation detailing how to install the application, configure API keys, and use the different functionalities provided.

To utilize the 'azure-ai-discovery' package effectively, you'll need to set up an Azure account and obtain the necessary credentials. Integrate these credentials securely within your application, ensuring they are not hard-coded into the source code. Use environment variables or a configuration file to manage sensitive data.

This project aims to showcase the capabilities of the Azure AI Discovery service while providing a practical tool for researchers, journalists, and anyone interested in exploring curated and diverse sources of information.

💬 Discussion Feed

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