AI Analysis
Final verdict: SUSPICIOUS
The package shows low individual risk scores across all checks, but the metadata risk due to a new or inactive maintainer account raises suspicion, especially given that the package is not intended for direct user installation.
- Low individual risk scores in network, shell, obfuscation, and credential risks.
- Metadata risk due to a new or inactive maintainer account.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell executions detected, indicating no direct system command risks.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has a new or inactive account with only one package, which could be suspicious but not necessarily malicious.
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 (258 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-image-components
Create a mini-application called 'ImageAnalyzer' that leverages the AzureML ACFT Image Components package to analyze images for various visual elements. The application should be designed as a command-line tool that accepts image file paths as input and outputs a detailed report about the image's content. Step 1: Set up your development environment with Python and install the necessary dependencies including the azureml-acft-image-components package. Step 2: Design the main functionality of the application where it loads an image from a specified path and uses the components provided by azureml-acft-image-components to detect and classify objects within the image. Step 3: Implement additional features such as: - Providing a summary of the most prominent objects detected in the image. - Allowing users to specify which types of objects they want to focus on detecting. - Offering an option to output the results in a structured format like JSON. Step 4: Ensure the application can handle errors gracefully, such as invalid file paths or unsupported image formats. Step 5: Write documentation and usage instructions for the command-line interface. How the 'azureml-acft-image-components' package is utilized: This package provides essential components for processing and analyzing images using machine learning models. Your application will use these components to perform tasks such as object detection and classification. For example, you might use specific functions from the package to preprocess images, extract features, and apply trained models to predict the presence and location of different objects in the image.
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