aws-resource-validator-rekognition

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk indicators but raises concerns due to incomplete author information and potentially inactive account.

  • Incomplete author information
  • Potentially inactive 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 detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The author's information is incomplete and the account seems new or inactive, which raises some concerns but not enough to definitively label it as malicious.

📦 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-rekognition 
Create a Python-based application that leverages the 'aws-resource-validator-rekognition' package to validate and manage Amazon Rekognition resources efficiently. This application will serve as a utility for developers and system administrators to ensure their AWS Rekognition configurations are compliant and optimized.

Step-by-Step Requirements:
1. Initialize your project with a virtual environment and install the required packages including 'aws-resource-validator-rekognition'.
2. Define a class structure using the Pydantic models provided by 'aws-resource-validator-rekognition' to represent different types of Rekognition resources such as collections, projects, and models.
3. Implement a function that takes in a configuration file (JSON format) containing details of Rekognition resources and validates it against the defined Pydantic models.
4. Develop a feature to automatically correct minor errors in the configuration file if possible, ensuring the validated data conforms to the Pydantic models.
5. Integrate the ability to generate a report summarizing the validation results, highlighting any issues found during the validation process.
6. Extend the application to support command-line interaction, allowing users to input paths to configuration files and select actions such as validation, correction, and reporting.
7. Optionally, implement a feature to compare two different configurations and highlight the differences between them.

Suggested Features:
- User-friendly CLI interface for easy interaction.
- Support for multiple configuration formats beyond JSON.
- Integration with AWS SDKs for direct resource management.
- Detailed error messages and suggestions for corrections.
- Logging capabilities for tracking validation activities.

How 'aws-resource-validator-rekognition' is Utilized:
This package provides Pydantic v2 models specifically designed for AWS Rekognition resources, which you'll use to define the structure of your configuration data. By leveraging these models, you can ensure that the configurations adhere to the expected schema, enhancing reliability and reducing human errors.

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