aws-resource-validator-medical-imaging

v2.0.3 safe
4.0
Medium Risk

Pydantic v2 models for AWS medical_imaging, shipped as a PEP 420 namespace extension of aws-resource-validator.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk with no network calls, shell executions, obfuscations, or credential harvesting. However, the incomplete author information and potential inactivity of the maintainer slightly increase the metadata risk.

  • No network calls
  • Incomplete author information
  • Potential inactivity of maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal for packages that don't require external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate threat to stored secrets.
  • Metadata: The author information is incomplete and the maintainer seems to be new or inactive, raising some concerns but not strong indicators of malice.

📦 Package Quality Overall: Low (3.8/10)

○ 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 (324 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 75 commits in CoreOxide/aws_resource_validator
  • Small but multi-author team (3–4 contributors)

🔬 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository CoreOxide/aws_resource_validator appears legitimate

⚠ 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 aws-resource-validator-medical-imaging 
Create a mini-application called 'Medical Imaging Validator' that leverages the 'aws-resource-validator-medical-imaging' Python package to validate medical imaging resources uploaded to AWS Medical Imaging service. This application should allow users to input metadata about their medical images and validate whether these resources meet specific criteria set by AWS Medical Imaging service standards.

The application should include the following features:
1. A user-friendly interface for inputting metadata such as image size, format, patient information, etc.
2. Integration with the 'aws-resource-validator-medical-imaging' package to validate the inputted metadata against AWS Medical Imaging service requirements.
3. Display validation results indicating whether the resource meets the required standards and provide feedback on any discrepancies found.
4. Optional feature: Allow users to upload actual medical images (simulated data) and validate them based on the provided metadata.
5. Detailed documentation explaining how to use the application and integrate it into existing workflows involving AWS Medical Imaging services.

Steps to create the application:
1. Set up a Python environment with the necessary dependencies including the 'aws-resource-validator-medical-imaging' package.
2. Design the user interface using a framework like Flask or Django for web-based applications or a simple command-line interface for console-based applications.
3. Implement the validation logic by utilizing the Pydantic v2 models provided by the 'aws-resource-validator-medical-imaging' package.
4. Test the application thoroughly with various sets of metadata and simulated medical images to ensure accuracy and reliability.
5. Package the application with detailed instructions and deploy it to a platform where it can be easily accessed and used by other developers or healthcare professionals.

💬 Discussion Feed

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