aws-resource-validator-sagemaker-a2i-runtime

v2.0.3 suspicious
4.0
Medium Risk

Pydantic v2 models for AWS sagemaker_a2i_runtime, shipped as a PEP 420 namespace extension of aws-resource-validator.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no direct signs of malicious activity, but the maintainer's incomplete profile and new account raise concerns about potential supply-chain risks.

  • Incomplete maintainer profile
  • New maintainer account
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, which is expected for a typical Python package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting legitimate use without compromising secrets.
  • Metadata: The maintainer has an incomplete profile and a new account, raising some suspicion but not definitive evidence 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 (342 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-sagemaker-a2i-runtime 
Develop a Python-based mini-application that leverages the 'aws-resource-validator-sagemaker-a2i-runtime' package to validate and manage Amazon SageMaker Augmented AI (A2I) workflows. This application will serve as a tool for developers and data scientists to ensure their A2I workflows are correctly configured before deployment. The app should include the following features:

1. **Workflow Validation**: Implement functionality to load a user-defined A2I workflow configuration file (in YAML format). Utilize the 'aws-resource-validator-sagemaker-a2i-runtime' package to validate the configuration against predefined Pydantic models. Ensure the application provides feedback on any validation errors or warnings.

2. **Interactive Workflow Editor**: Integrate a simple text-based or graphical interface where users can edit their A2I workflow configurations. This editor should dynamically highlight potential issues based on real-time validation using the 'aws-resource-validator-sagemaker-a2i-runtime' package.

3. **Suggested Improvements**: After validating a workflow, the application should suggest improvements or best practices for configuring the A2I workflow, enhancing its efficiency and reliability.

4. **Export Validated Workflows**: Allow users to export validated workflows into a new YAML file or directly upload them to an S3 bucket. Ensure that the exported workflows are free from any validation issues identified during the validation process.

5. **Documentation and Examples**: Provide comprehensive documentation and examples within the application to guide users through setting up and validating A2I workflows effectively.

By utilizing the 'aws-resource-validator-sagemaker-a2i-runtime' package, your application will ensure that developers and data scientists can create robust and error-free A2I workflows, enhancing the overall quality and performance of their machine learning projects.

💬 Discussion Feed

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