aws-resource-validator-arc-zonal-shift

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell, obfuscation, and credential handling, but the metadata risk score is elevated due to the maintainer's new or inactive account and lack of proper identification.

  • Low risk in functional areas
  • Elevated metadata risk due to maintainer's profile
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution detected, indicating the package does not perform system-level commands.
  • 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 a new or inactive account and lacks a proper author name, which may indicate a less experienced or potentially suspicious actor.

📦 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 (324 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-arc-zonal-shift 
Create a Python-based CLI tool named 'ZonalShiftChecker' that leverages the 'aws-resource-validator-arc-zonal-shift' package to validate AWS resources specifically designed for Amazon Aurora across different Availability Zones (AZs). This tool will allow users to input details about their Aurora databases and validate if the configurations comply with best practices for zonal shifts and disaster recovery. Here’s a detailed breakdown of what the tool should include:

1. **User Input Handling**: The tool should accept inputs from the user through command-line arguments or a simple configuration file. Users should be able to specify the region, database cluster identifier, and other relevant parameters like read replicas and cross-AZ deployments.

2. **Validation Logic**: Utilize the 'aws-resource-validator-arc-zonal-shift' package to validate the provided resource configurations against predefined rules and best practices. Ensure that the validation checks cover aspects such as data replication across AZs, availability of read replicas in multiple AZs, and compliance with Aurora's high availability requirements.

3. **Output Reporting**: After performing the validations, the tool should generate a report detailing any issues found and recommendations for improvement. The output should be both human-readable and machine-readable (JSON format).

4. **Customizable Rules**: Allow users to customize the validation rules based on their specific needs. This could involve adding or modifying checks related to security groups, VPC configurations, or additional compliance standards.

5. **Integration with AWS SDK**: To fetch real-time information about the AWS resources, integrate the tool with the Boto3 library, which is the AWS SDK for Python. This will ensure that the tool works seamlessly with live AWS environments.

6. **Testing Framework**: Implement a comprehensive testing framework using pytest to ensure the reliability and robustness of the tool. Include tests for various scenarios, including edge cases and invalid configurations.

7. **Documentation**: Provide clear documentation on how to install, configure, and use the tool. Include examples and best practices for setting up Aurora databases in a way that maximizes availability and disaster recovery capabilities.

The 'aws-resource-validator-arc-zonal-shift' package plays a crucial role in this project by providing Pydantic models that define the structure and constraints for validating AWS Aurora resources. These models will help in ensuring that the configurations adhere to AWS best practices and are optimized for performance and resilience.

💬 Discussion Feed

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