aws-resource-validator-lambda

v2.0.3 safe
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all evaluated categories except for metadata, where there is some concern regarding the maintainer's information. However, the absence of any direct malicious activities suggests it is safe to use.

  • Low risk in network, shell execution, and obfuscation
  • Incomplete maintainer information and potentially inactive account
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author information is incomplete and the account seems new or inactive, which may indicate a lack of trustworthiness.

📦 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 (298 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-lambda 
Your task is to develop a simple web-based utility using Flask and the 'aws-resource-validator-lambda' package. This utility will allow users to input AWS Lambda function configurations and validate them against predefined schemas provided by the package. The goal is to ensure that the configurations adhere to best practices and standards set by AWS, thereby reducing deployment errors.

### Steps to Complete the Project:
1. **Set Up Your Environment:** Ensure you have Python installed along with Flask and install the 'aws-resource-validator-lambda' package via pip.
2. **Define User Interface:** Create a basic HTML form where users can input their AWS Lambda configuration details.
3. **Backend Integration:** Use Flask to handle HTTP requests and responses. When a user submits the form, your backend should receive the data, process it using the 'aws-resource-validator-lambda' package, and return validation results.
4. **Validation Logic:** Utilize the Pydantic v2 models from 'aws-resource-validator-lambda' to validate the incoming configurations. Implement error handling to gracefully manage invalid inputs.
5. **Display Results:** Show the validation results back to the user on the same page, indicating whether the configuration is valid or not, and provide specific feedback on any issues found.
6. **Testing:** Thoroughly test your application with various valid and invalid configurations to ensure robustness.

### Suggested Features:
- **Real-time Validation Feedback:** As users fill out the form, provide instant feedback on whether the fields are correctly formatted according to the AWS standards.
- **Detailed Error Messages:** For each validation failure, display a detailed message explaining why the configuration is invalid.
- **Configuration Examples:** Include a section that provides sample configurations for users to refer to.
- **Deployment Guide:** Offer guidance on deploying validated configurations to AWS Lambda.

This project aims to streamline the process of validating AWS Lambda configurations, ensuring they meet necessary criteria before actual deployment, thus saving time and reducing potential issues.

💬 Discussion Feed

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