aws-resource-validator-bedrock-data-automation

v2.0.3 suspicious
4.0
Medium Risk

Pydantic v2 models for AWS bedrock_data_automation, 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 maintainer's new or inactive account and lack of author information slightly increase the risk level.

  • New or inactive maintainer account
  • No author name provided
Per-check LLM notes
  • Network: No network calls detected, which is expected if the package does not require external API interactions.
  • 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 has a new or inactive account with minimal package history and no author name provided, which raises some suspicion but not enough to conclude 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 (348 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-bedrock-data-automation 
Create a Python-based command-line tool that validates AWS Bedrock Data Automation resources using the 'aws-resource-validator-bedrock-data-automation' package. This tool will allow users to input their AWS resource configurations and validate them against predefined schemas provided by the package. The goal is to ensure that the configurations adhere to best practices and are correctly formatted before deployment. Here are the steps and features you need to implement:

1. **Setup**: Initialize your project with a virtual environment and install the necessary packages including 'aws-resource-validator-bedrock-data-automation'.
2. **Input Configuration**: Develop a function that allows users to input their AWS resource configurations either via a file upload or directly through the command line.
3. **Validation**: Utilize the Pydantic v2 models from 'aws-resource-validator-bedrock-data-automation' to validate the input configuration. Ensure that the validation process checks for common issues like missing required fields, incorrect data types, and adherence to AWS standards.
4. **Output Feedback**: After validation, provide feedback to the user indicating whether the configuration is valid or not. If there are errors, clearly list them out so the user knows exactly what needs to be corrected.
5. **Additional Features**:
   - Include an option to automatically correct minor issues (e.g., adding default values where applicable).
   - Implement a feature to generate sample configurations based on the validated schemas.
   - Add support for different AWS regions and services within Bedrock Data Automation.
6. **Documentation**: Write comprehensive documentation explaining how to use the tool, including examples of valid and invalid configurations.
7. **Testing**: Create test cases to ensure your tool works as expected across various scenarios.
8. **Deployment**: Package your tool into a distributable format such as a pip package or Docker image for easy distribution.

By following these steps, you'll create a valuable utility that helps developers and DevOps engineers ensure their AWS Bedrock Data Automation configurations are robust and error-free.

💬 Discussion Feed

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