aws-resource-validator-emr-serverless

v2.0.3 safe
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of network calls, shell executions, obfuscation, or credential harvesting. However, the metadata risk score is elevated due to the maintainer's account status, which slightly increases suspicion.

  • Low risk scores across all technical indicators
  • Elevated metadata risk due to maintainer's account status
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating the package likely does not perform system-level operations.
  • 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 and lacks author information, which raises some concerns but does not strongly indicate malicious intent.

📦 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 (321 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-emr-serverless 
Create a Python-based command-line utility named 'EMR-Serverless Validator' which leverages the 'aws-resource-validator-emr-serverless' package to validate configurations for AWS EMR Serverless applications. This utility should enable users to input their EMR Serverless configuration details via a simple interface and receive validation feedback immediately. The goal is to ensure configurations adhere to best practices and are syntactically correct before deployment.

Steps to Build the Utility:
1. **Setup Project Structure**: Create a directory for your project, initialize a virtual environment, and install necessary packages including 'aws-resource-validator-emr-serverless'.
2. **Define Configuration Model**: Use Pydantic v2 models provided by 'aws-resource-validator-emr-serverless' to define the structure of valid EMR Serverless configurations.
3. **Input Interface**: Develop a CLI where users can enter their EMR Serverless configurations. This could be done through command line arguments or a simple text file upload option.
4. **Validation Logic**: Implement logic that takes the user-provided configuration, validates it against the defined model using 'aws-resource-validator-emr-serverless', and outputs any errors or warnings.
5. **Output Feedback**: Display clear, actionable feedback to the user about the validity of their configuration. Highlight any issues and suggest corrections if possible.
6. **Testing**: Ensure thorough testing of your utility with various configurations to catch edge cases and ensure robustness.
7. **Documentation**: Write comprehensive documentation detailing how to use the utility, including examples and common pitfalls.

Suggested Features:
- Support for both synchronous and asynchronous validation modes.
- Ability to validate multiple configurations at once.
- Integration with AWS SDK for Python (Boto3) to fetch existing configurations for comparison and validation.
- User-friendly error messages that not only indicate failure but also provide guidance on fixing issues.
- Logging capabilities to record validation attempts and outcomes.

How 'aws-resource-validator-emr-serverless' is Utilized:
This package provides pre-defined Pydantic models tailored specifically for AWS EMR Serverless resources, making it easier to validate configurations against AWS standards. By utilizing these models, you can ensure that the configurations submitted by users conform to expected schemas, thus reducing the likelihood of errors during deployment.

💬 Discussion Feed

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