aws-resource-validator-importexport

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious activity such as network calls, shell executions, or credential harvesting. However, the incomplete author details and the potential inactivity of the maintainer raise concerns about its origin and maintenance.

  • Incomplete author details
  • Potential inactivity of the maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package is expected to communicate with external services like AWS.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author details are incomplete and the maintainer seems to be new or inactive, raising some suspicion but not conclusive 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 (315 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-importexport 
Create a Python-based command-line tool named 'AWS ImportExport Validator' that leverages the 'aws-resource-validator-importexport' package to validate AWS Import/Export jobs. This tool will allow users to input details of their AWS Import/Export job requests and receive validation feedback on whether the job request adheres to AWS specifications. The application should be designed with user-friendliness in mind, ensuring ease of use and clear output messages.

**Step-by-Step Guide**:
1. **Setup**: Begin by setting up a Python environment with the necessary dependencies including 'aws-resource-validator-importexport'. Ensure you have the latest version of Pydantic installed as well.
2. **Input Handling**: Develop a module to handle user inputs. Users should be able to provide job request parameters such as 'jobType', 'manifestFileLocation', 'validateManifest', etc., either through command-line arguments or a simple text file input.
3. **Validation Logic**: Utilize the 'aws-resource-validator-importexport' package to create validation logic. This involves using the Pydantic models provided by the package to validate the input against AWS standards. Each model corresponds to specific parts of the job request, such as the manifest file structure or job settings.
4. **Output Feedback**: Implement a feedback system that clearly communicates validation results back to the user. If the job request passes validation, the tool should confirm this and perhaps even suggest next steps. If there are issues, provide detailed error messages explaining what needs to be corrected.
5. **Error Handling**: Incorporate robust error handling to manage any unexpected inputs or issues gracefully, providing clear instructions to help users troubleshoot.
6. **Testing**: Write comprehensive tests to ensure your tool works as expected across different scenarios. Pay special attention to edge cases where inputs might be slightly off from AWS standards but still acceptable.
7. **Documentation**: Finally, write clear documentation for your tool, explaining how to install it, how to use it, and what each part of the output means. Include examples of valid and invalid inputs to help users understand common pitfalls.

**Suggested Features**:
- Support for both batch and single-file validation processes.
- Option to automatically generate sample job request files based on the AWS standards.
- Integration with AWS SDKs for Python (Boto3) to directly send validated job requests to AWS.
- A progress bar or status indicator during the validation process.
- Localization support for different languages.

This project not only serves as a practical application of the 'aws-resource-validator-importexport' package but also as a valuable tool for developers working with AWS Import/Export services.

💬 Discussion Feed

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