aws-resource-validator-partnercentral-selling

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal direct risks but lacks complete author information, raising concerns about its legitimacy and potential for supply-chain attacks.

  • Incomplete author information
  • Potential supply-chain attack vector
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author information is incomplete, which raises some concern about the legitimacy of the package.

📦 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 (345 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-partnercentral-selling 
Create a Python-based utility named 'ResourceChecker' that leverages the 'aws-resource-validator-partnercentral-selling' package to validate and manage AWS resources associated with the Partner Central Selling program. This tool will help users ensure their AWS resources comply with specific validation rules defined within the Partner Central Selling context. Here’s a detailed plan for your project:

1. **Setup Environment**: Begin by setting up a virtual environment and installing necessary packages including 'aws-resource-validator-partnercentral-selling'. Ensure you have the latest version of Pydantic installed as well.

2. **Define Core Functionality**: The primary function of ResourceChecker should be to take an input file (JSON format) containing AWS resource definitions and validate them against predefined schemas provided by the 'aws-resource-validator-partnercentral-selling' package. This includes checking for syntax correctness, required fields, and data type conformity.

3. **User Interface**: Implement a simple command-line interface (CLI) where users can interact with ResourceChecker. They should be able to specify the input file and receive validation results directly in the console. Consider adding options for verbosity to provide more detailed feedback during validation processes.

4. **Error Handling & Reporting**: Design comprehensive error handling mechanisms to catch any issues encountered during validation. Output should clearly distinguish between syntactical errors, missing required fields, and type mismatches. Provide suggestions on how to correct these errors whenever possible.

5. **Integration with AWS SDKs**: Extend functionality by integrating with AWS SDKs to automatically fetch resource configurations from AWS services (if applicable), allowing for real-time validation checks without manual input files.

6. **Documentation & Testing**: Write clear documentation explaining how to use ResourceChecker, including examples of valid and invalid inputs. Implement unit tests using frameworks like pytest to ensure robustness and reliability of your application.

7. **Advanced Features**: As an enhancement, consider adding features such as automatic correction for minor issues (e.g., fixing indentation errors), support for multiple input formats (besides JSON), and integration with CI/CD pipelines for automated validation during development cycles.

💬 Discussion Feed

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