aws-resource-validator-ecr

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has no direct risks such as network calls, shell execution, or obfuscation. However, the incomplete author metadata and potential inactivity raise some suspicion.

  • Incomplete author metadata
  • Potential inactivity of the author
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services like AWS ECR.
  • Shell: No shell execution patterns detected, which aligns with an expected behavior for a Python package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The author's details are incomplete and the author seems to be new or inactive, which raises some concerns but does not strongly indicate 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 (288 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-ecr 
Create a mini-application named 'EcrValidator' that leverages the 'aws-resource-validator-ecr' package to validate and manage Docker images stored in Amazon ECR repositories. This tool will serve as a utility for developers and DevOps engineers to ensure their Docker images meet specific criteria before deployment. Here’s a detailed breakdown of the steps and features required:

1. **Setup and Installation**: Begin by setting up a virtual environment for your project. Install the necessary dependencies, including 'aws-resource-validator-ecr', boto3 for AWS SDK, and any other required Python packages.

2. **Authentication**: Implement a function that allows users to authenticate with AWS using IAM roles or access keys. Ensure that the application securely handles credentials.

3. **Repository Management**: Develop functionality to list all repositories within a specified AWS account or region. Users should be able to select a repository for further operations.

4. **Image Validation**: Utilize the Pydantic models provided by 'aws-resource-validator-ecr' to validate Docker images against predefined schemas. These schemas could include checks such as image tag format, image size limits, and security compliance rules.

5. **Security Scanning**: Integrate with AWS ECR’s built-in scanning capabilities to perform vulnerability scans on Docker images. Display the scan results and allow users to decide whether to proceed with the image based on these results.

6. **Tagging and Versioning**: Provide options for tagging images with semantic version numbers or custom tags. Ensure that the application supports automatic tagging based on certain criteria.

7. **Push and Pull Operations**: Implement functions to push validated images to an ECR repository and pull images from it. Include error handling and progress tracking for these operations.

8. **CLI Interface**: Design a command-line interface (CLI) for the application, making it easy for users to run validations, manage repositories, and perform other operations without needing a graphical user interface.

9. **Logging and Reporting**: Incorporate logging to track operations performed by the application. Additionally, generate reports summarizing the validation outcomes, security findings, and repository activities.

10. **Documentation and Testing**: Write comprehensive documentation for the application, detailing how to install, configure, and use each feature. Also, develop unit tests and integration tests to ensure the reliability of the application.

By following these steps and incorporating the above features, you will create a robust, secure, and user-friendly tool that significantly simplifies the management and validation of Docker images in AWS ECR.

💬 Discussion Feed

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