aws-resource-validator-kinesisanalytics

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package appears to be well-structured and without immediate malicious intent, but the metadata risk due to the maintainer's incomplete profile and new account raises some suspicion.

  • Low risk in terms of network, shell, obfuscation, and credential handling.
  • Metadata risk due to the maintainer's incomplete profile and new account.
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, suggesting legitimate use.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of secrets.
  • Metadata: The maintainer has an incomplete profile and a new account with only one package, 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 (327 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-kinesisanalytics 
Develop a small utility named 'KinesisAnalyticsValidator' using Python that leverages the 'aws-resource-validator-kinesisanalytics' package to validate and manage AWS Kinesis Analytics resources. This tool should allow users to define their Kinesis Analytics applications, data streams, and other related resources using Pydantic models provided by the package. Here’s a detailed step-by-step guide on how to build this utility:

1. **Setup Environment**: Start by setting up a virtual environment for your project. Install the required packages including 'aws-resource-validator-kinesisanalytics'.
2. **Define Models**: Use the Pydantic models from 'aws-resource-validator-kinesisanalytics' to define your AWS Kinesis Analytics resources such as ApplicationInput, ApplicationOutput, etc. Customize these models if necessary to fit your specific use case.
3. **Validation Functionality**: Implement functions that validate user-defined resources against the defined Pydantic models. Ensure these functions provide clear error messages if validation fails.
4. **Resource Management**: Add functionality to create, update, and delete Kinesis Analytics resources based on validated input. Utilize Boto3, AWS SDK for Python, to interact with AWS services.
5. **CLI Interface**: Develop a command-line interface (CLI) for your utility. Users should be able to run commands like `kinesis-analytics-validator create`, `kinesis-analytics-validator update`, and `kinesis-analytics-validator delete` to manage their resources.
6. **Documentation**: Write comprehensive documentation explaining how to install the utility, define resources, and use the CLI. Include examples for each type of resource management operation.
7. **Testing**: Implement unit tests and integration tests to ensure your utility works as expected under various scenarios.
8. **Deployment**: Package your utility as a Python package and deploy it to PyPI so others can easily install and use it.

Suggested Features:
- Support for both V1 and V2 Kinesis Analytics applications.
- Detailed logging and error handling mechanisms.
- Configuration file support for defining default settings.
- Extensibility to add custom validators and resource types.

By following these steps, you will have developed a powerful and flexible utility that simplifies the process of managing AWS Kinesis Analytics resources.

💬 Discussion Feed

Leave a comment

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