azureml-acft-multimodal-components

v0.0.91 suspicious
4.0
Medium Risk

Contains the acft multimodal-contrib package used in script to build azureml components.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal direct risks but raises concerns due to its unexpected presence on PyPI and the lack of supporting metadata.

  • No network calls or shell executions detected
  • Author's account is new or inactive
  • No associated GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package that does not require external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate threat to secrets or credentials.
  • Metadata: The author has a new or inactive account and no associated GitHub repository, which may indicate a lack of community support or maintenance.

📦 Package Quality Overall: Low (2.0/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Brief PyPI description (213 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ 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

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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 2.0

1 maintainer concern(s) found

  • Author "Microsoft Corp" 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 azureml-acft-multimodal-components 
Create a multimedia content analysis tool using the 'azureml-acft-multimodal-components' package. This tool will enable users to upload both images and audio files, and then analyze these media types together to extract meaningful insights and metadata. Here’s a detailed breakdown of the steps and features for this project:

1. **Project Setup**: Begin by setting up your Python environment and installing necessary packages including 'azureml-acft-multimodal-components'. Ensure Azure Machine Learning Workspace is properly configured.

2. **User Interface**: Develop a simple web interface where users can upload their image and audio files. Use Flask or Django for backend development and integrate it with frontend technologies like HTML, CSS, and JavaScript for a responsive UI.

3. **File Upload Handling**: Implement functionality to handle file uploads from users. Ensure the system supports various file formats for both images and audio.

4. **Multimedia Analysis**: Utilize 'azureml-acft-multimodal-components' to process and analyze the uploaded media files. This includes extracting features such as visual objects from images, and audio characteristics like speech content or music genre from audio files.

5. **Insight Generation**: Based on the analysis, generate insights about the content. For example, identify objects within images, transcribe spoken words from audio, or classify music genres.

6. **Metadata Extraction**: Extract relevant metadata from both image and audio files, such as EXIF data from images or ID3 tags from audio files.

7. **Results Display**: Present the extracted insights and metadata back to the user through the web interface in an easily digestible format.

8. **Error Handling & Logging**: Implement robust error handling to manage any issues during file processing and logging to track system operations.

9. **Security Considerations**: Ensure secure handling of user data, including encryption of sensitive information and compliance with privacy laws.

By following these steps and utilizing the capabilities of 'azureml-acft-multimodal-components', you'll create a powerful tool for analyzing multimedia content, making it easier for users to understand and manage their media files.

💬 Discussion Feed

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