azure-ai-projects

v2.2.0 suspicious
6.0
Medium Risk

Microsoft Corporation Azure AI Projects Client Library for Python

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to its high network and shell execution risks, despite having low credential and metadata risks. The unusual network behavior and potential for executing shell commands raise concerns about its legitimacy and safety.

  • High network risk due to OpenAI logging transport
  • High shell risk indicating potential for arbitrary command execution
Per-check LLM notes
  • Network: The use of OpenAI logging transport is unusual and may indicate unexpected network behavior not aligned with Azure services.
  • Shell: Executing shell commands can be legitimate but also risky as it allows for arbitrary command execution which could be exploited.
  • Obfuscation: The detected patterns appear to be related to deserialization and path extension, which are not inherently malicious but could indicate obfuscation techniques.
  • Credentials: No suspicious patterns for credential harvesting were detected.
  • Metadata: The package has some minor red flags, such as an author with no name and possibly a new account, but there are no clear signs of malicious intent or typosquatting.

📦 Package Quality Overall: Low (3.2/10)

○ 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 (54965 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

  • 219 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)

  • g_enabled: return httpx.Client(transport=_OpenAILoggingTransport()) return None
  • g_enabled: return httpx.AsyncClient(transport=_OpenAILoggingTransport()) return None
⚠ 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 score 2.0

Found 1 shell execution pattern(s)

  • dacted)) completed = subprocess.run(cmd, check=False, capture_output=True, text=True) if
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ 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-ai-projects 
Develop a mini-application that integrates Microsoft Azure Cognitive Services using the 'azure-ai-projects' Python package to create an interactive image captioning tool. This application will allow users to upload images and receive descriptive captions generated by Azure's AI services. Here’s a detailed breakdown of the steps and features to include in your project:

1. **Setup**: Ensure you have an Azure account and the necessary Cognitive Services resources (such as Computer Vision API) set up.
2. **Project Initialization**: Create a new Python project and install the 'azure-ai-projects' package.
3. **Authentication**: Implement authentication logic to securely interact with Azure services using your subscription key and endpoint URL.
4. **User Interface**: Design a simple yet user-friendly interface where users can upload images.
5. **Image Captioning Functionality**: Utilize the 'azure-ai-projects' package to send uploaded images to Azure's Computer Vision API for analysis and caption generation.
6. **Display Captions**: Once processed, display the generated captions back to the user on the interface.
7. **Enhancements**: Consider adding features such as saving the image-caption pairs locally or cloud-based, providing options to edit generated captions, or integrating additional AI functionalities like emotion detection from the image.
8. **Testing and Validation**: Test the application thoroughly with various types of images to ensure accuracy and reliability of the captioning service.
9. **Documentation**: Provide clear documentation explaining how to run the application, including setup instructions and usage guidelines.

By following these steps, you’ll create a practical and engaging mini-application that leverages Azure's powerful AI capabilities through the 'azure-ai-projects' package.

💬 Discussion Feed

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