aws-resource-validator-wickr

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network, shell, and obfuscation activities, with no direct signs of malicious behavior. However, the metadata risk due to missing maintainer information and possibly inactive account raises some suspicion.

  • Low network, shell, and obfuscation risks
  • Missing maintainer's author name and possibly inactive account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires online resources or services.
  • 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 maintainer's author name is missing and the account seems new or inactive, 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 (294 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-wickr 
Develop a command-line tool named 'WickrValidator' using Python that leverages the 'aws-resource-validator-wickr' package to validate AWS resources against Wickr-specific requirements. This tool will help users ensure their AWS configurations meet the necessary standards for secure communication using Wickr services. Here are the key steps and features for your project:

1. **Setup**: Start by installing the 'aws-resource-validator-wickr' package and any other necessary dependencies like Pydantic v2 and Boto3 for AWS interactions.

2. **Configuration**: Allow users to input their AWS credentials securely. Implement functionality to load these credentials from environment variables or a configuration file for better security practices.

3. **Resource Validation**: Utilize the Pydantic v2 models provided by 'aws-resource-validator-wickr' to define validation rules specific to Wickr's requirements. These rules should cover aspects such as IAM policies, S3 bucket permissions, and other relevant AWS resources.

4. **Validation Process**: Create a CLI command that accepts a list of AWS resource types (e.g., S3 buckets, IAM roles) and performs validation checks against the defined models. Display clear, actionable feedback on any discrepancies found during the validation process.

5. **Report Generation**: Implement a feature to generate a comprehensive report after the validation process. This report should include details about each resource, whether it passed or failed the validation, and any corrective actions needed.

6. **Security Enhancements**: Since security is paramount, ensure all data exchanges are encrypted and that sensitive information is handled securely throughout the tool’s operation.

7. **Testing and Documentation**: Write thorough unit tests to cover various scenarios including edge cases. Provide clear documentation detailing how to install, configure, and use the tool effectively.

By following these steps, you'll create a valuable tool for anyone looking to integrate Wickr services into their AWS infrastructure while ensuring compliance with Wickr's stringent security requirements.

💬 Discussion Feed

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