aws-resource-validator-databrew

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network, shell, obfuscation, and credential areas, but the metadata risk score is elevated due to sparse author information and limited maintainer presence, raising concerns about potential supply-chain risks.

  • Metadata risk score is 3 out of 10, indicating sparse author information and limited maintainer presence.
  • No direct evidence of malicious activities, but elevated metadata risk warrants further investigation.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on local validation.
  • Shell: No shell execution patterns detected, aligning with the expected behavior of a validation tool.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author information is sparse and the maintainer has a limited presence on PyPI, which raises some suspicion but does not definitively indicate malicious intent.

📦 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 (303 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-databrew 
Create a mini-application that serves as a data validation tool for AWS Databrew using the 'aws-resource-validator-databrew' package. This application will allow users to validate AWS Databrew resources such as recipes, jobs, and datasets against predefined schemas to ensure they meet certain criteria before deployment. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Ensure your development environment has Python installed along with the necessary packages including 'aws-resource-validator-databrew'. Use virtual environments to manage dependencies.

2. **Define Validation Scenarios**: Using the Pydantic v2 models provided by 'aws-resource-validator-databrew', define various validation scenarios for different types of AWS Databrew resources. For example, create models for validating the structure and completeness of job configurations.

3. **Implement Data Fetching Mechanism**: Develop a mechanism within the application to fetch AWS Databrew resources either from local files or directly from AWS services. Consider using Boto3, the AWS SDK for Python, to interact with AWS APIs.

4. **Validation Logic**: Integrate the fetched data with the defined validation scenarios. Implement logic to validate each resource against its corresponding model. Ensure that the validation process is efficient and provides clear error messages when a resource does not comply with the schema.

5. **User Interface**: Although not mandatory, consider adding a simple command-line interface (CLI) or even a web-based UI if you're comfortable with frameworks like Flask or Django. This will make it easier for users to input their AWS credentials, select which resources to validate, and view the results.

6. **Reporting**: After validation, generate a report detailing which resources passed and which failed validation. Include suggestions for fixing any issues found during validation.

7. **Testing**: Rigorously test the application with different AWS Databrew resources to ensure robustness and reliability.

8. **Documentation**: Write comprehensive documentation explaining how to use the application, including setup instructions and examples of valid and invalid AWS Databrew resources.

Optional Features:
- Integration with AWS Lambda for automated validation.
- Support for multiple AWS accounts and regions.
- Real-time monitoring and alerting for failed validations.

This project aims to streamline the validation process of AWS Databrew resources, ensuring they adhere to best practices and standards before being deployed in production.

💬 Discussion Feed

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