aws-resource-validator-autoscaling

v2.0.3 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity, with very low risks across all categories except metadata, where the maintainer's information is incomplete.

  • No network calls detected.
  • No shell execution patterns.
  • No obfuscation or credential harvesting.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating the package likely 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 information is incomplete, which may indicate a lack of transparency or a new and less active account.

📦 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 (312 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-autoscaling 
Create a Python-based utility called 'AutoScaleChecker' that leverages the 'aws-resource-validator-autoscaling' package to validate and monitor Auto Scaling Groups (ASGs) within an AWS environment. This tool will help users ensure their ASGs adhere to best practices and are configured correctly according to predefined rules. Here’s how you can build it step-by-step:

1. **Setup**: Install the necessary Python packages including 'aws-resource-validator-autoscaling', 'boto3' for AWS SDK, and 'pydantic' for data validation.
2. **Configuration**: Allow users to configure their AWS credentials via environment variables or a configuration file. Ensure these credentials have the necessary permissions to access the Auto Scaling Groups.
3. **Data Retrieval**: Use boto3 to fetch details about all Auto Scaling Groups within a specified region or account.
4. **Validation Logic**: Utilize the 'aws-resource-validator-autoscaling' package to define validation rules based on Pydantic models. These rules could include checking if the desired capacity, minimum size, and maximum size are set appropriately, ensuring proper health checks are enabled, etc.
5. **Reporting**: Implement a reporting feature where the utility outputs a detailed report of each ASG, indicating whether it passes or fails the validation checks. This report should also suggest improvements for failing ASGs.
6. **CLI Interface**: Develop a command-line interface (CLI) for the utility, allowing users to run validations with specific options such as targeting particular regions or ASGs.
7. **Logging & Error Handling**: Integrate logging capabilities to record actions and errors encountered during execution. Ensure graceful handling of exceptions like network issues or permission denials.
8. **Testing**: Write tests to validate the functionality of your utility, covering edge cases and typical use scenarios.

Suggested Features:
- Option to automatically correct minor issues detected during validation (e.g., adjusting min/max sizes).
- Support for multiple AWS accounts by specifying different profiles.
- Real-time monitoring mode that continuously checks ASGs at regular intervals.
- Integration with AWS CloudWatch for alerting on failed validations.

The 'aws-resource-validator-autoscaling' package is crucial here as it provides pre-defined Pydantic models that align closely with AWS Auto Scaling Group structures. By leveraging these models, you can easily implement complex validation logic without having to manually parse and understand the intricacies of AWS resource configurations.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!