azureml-acft-common-components

v0.0.91 suspicious
4.0
Medium Risk

Basic implementation of common components and utility functions that can be used by all verticals (image/video/nlp/multi-modal).

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risks in terms of network usage, shell execution, and obfuscation techniques. However, the metadata risk score is elevated due to the author's new or inactive account and lack of a linked GitHub repository.

  • Metadata risk due to new/inactive author account
  • No linked GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has a new or inactive account and there's no linked GitHub repository, which raises some suspicion but not enough to conclude malice.

📦 Package Quality Overall: Low (1.2/10)

○ 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
○ 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-common-components 
Create a mini-application named 'MultiModalAnalyzer' using Python and the 'azureml-acft-common-components' package. This application will serve as a versatile tool for analyzing multimodal data, combining image and text inputs to provide comprehensive insights. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Ensure you have Python installed along with the necessary libraries including 'azureml-acft-common-components'. Use pip or conda to install any missing packages.

2. **Project Structure**: Organize your project into several directories such as 'src', 'data', 'models', and 'utils'. Within 'src', create subdirectories like 'image_processing', 'text_analysis', and 'multimodal_analysis'.

3. **Image Processing**: Implement functions to preprocess images (e.g., resizing, normalization). Utilize the 'azureml-acft-common-components' package for basic image processing utilities.

4. **Text Analysis**: Develop methods for cleaning and analyzing text data. Use the 'azureml-acft-common-components' for common NLP tasks like tokenization and sentiment analysis.

5. **Multimodal Analysis**: Combine the outputs from both image and text analyses to generate a unified report. For instance, if an image shows a smiling person and the associated text is positive, the application could infer a positive sentiment.

6. **User Interface**: Design a simple web interface using Flask or Django where users can upload an image and text, and receive the analyzed results. Display the processed image, key findings from the text analysis, and the combined multimodal analysis.

7. **Testing & Documentation**: Write tests for each component of your application to ensure reliability. Provide clear documentation detailing how to set up the environment, run the application, and interpret the output.

Suggested Features:
- Support for multiple languages in text analysis.
- Option to highlight specific regions in images based on text content.
- Ability to save and load analysis results.

Utilize 'azureml-acft-common-components' to streamline your development process, leveraging its utility functions and common components to handle complex tasks efficiently.

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