aws-resource-validator-frauddetector

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk in terms of network activity, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is elevated due to the author's new or inactive account and lack of proper identification, raising suspicion.

  • Metadata risk due to author's account status
  • Lack of proper author identification
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on local validation.
  • Shell: No shell execution patterns detected, consistent with a benign utility.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has a new or inactive account and lacks a proper author name, which could indicate potential risk.

📦 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 (318 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-frauddetector 
Create a Python-based mini-application that integrates with AWS Fraud Detector using the 'aws-resource-validator-frauddetector' package. This application will serve as a validation tool for resources related to AWS Fraud Detector, ensuring they adhere to best practices and standards. Here's a detailed plan on how to proceed:

1. **Project Setup**: Begin by setting up your Python environment. Install necessary packages including 'aws-resource-validator-frauddetector', Boto3 (AWS SDK for Python), and any other dependencies.
2. **Authentication & Configuration**: Implement AWS authentication using IAM roles or access keys. Ensure your application can securely interact with AWS services.
3. **Resource Validation**: Utilize the 'aws-resource-validator-frauddetector' package to define validation rules and schemas for various AWS Fraud Detector resources such as detectors, labels, entities, and event types. This package leverages Pydantic v2 models to ensure data consistency and integrity.
4. **API Integration**: Develop a RESTful API that allows users to submit their AWS Fraud Detector configurations for validation. Users should be able to upload their configuration files or input them directly into the API.
5. **Validation Logic**: Upon receiving a request, your application should validate the provided resource against the defined schemas. It should provide detailed feedback on compliance issues, suggesting corrections where possible.
6. **User Interface**: Optionally, develop a simple web interface (using Flask or Django) to make it easier for non-technical users to validate their AWS Fraud Detector configurations.
7. **Documentation & Testing**: Write comprehensive documentation explaining how to use your application. Include examples of valid and invalid configurations. Conduct thorough testing to ensure robustness and reliability.
8. **Deployment**: Deploy your application on a cloud service like AWS Lambda or Heroku, making it accessible to a wider audience.

Suggested Features:
- Real-time validation feedback
- Automated correction suggestions for common errors
- Detailed error reporting and logging
- Support for multiple AWS regions and accounts
- Integration with CI/CD pipelines for automated validation during deployment phases

💬 Discussion Feed

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