aws-resource-validator-lakeformation

v2.0.3 safe
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity such as network calls, shell execution, or obfuscation. However, there are some concerns regarding the incomplete author details and potential inactivity of the maintainer.

  • No network calls detected
  • Incomplete author details
  • Potential inactivity of maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author details are incomplete and the maintainer seems to be new or inactive, which raises some concerns but not enough to strongly suggest 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 (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-lakeformation 
Create a command-line tool called 'LakeFormationChecker' using Python that leverages the 'aws-resource-validator-lakeformation' package to validate and audit AWS LakeFormation resources. This tool will help users ensure their LakeFormation configurations meet specific criteria, such as correct IAM permissions, resource tagging, and compliance with organizational policies. Here are the steps and features you need to implement:

1. **Setup**: Install the necessary Python packages, including 'aws-resource-validator-lakeformation'. Ensure your AWS credentials are configured properly.
2. **Resource Fetching**: Implement a function that fetches all relevant LakeFormation resources from a specified AWS account. This includes databases, tables, and permissions.
3. **Validation Logic**: Use the 'aws-resource-validator-lakeformation' package to define validation rules. These rules should check for things like proper tagging, correct IAM roles attached, and adherence to naming conventions.
4. **Audit Report Generation**: After fetching and validating resources, generate a detailed report summarizing the findings. This report should include a list of compliant and non-compliant resources, along with reasons for non-compliance.
5. **User Interface**: Design a simple and intuitive command-line interface for users to interact with the tool. Users should be able to specify which resources to check and where to output the audit report.
6. **Configuration File Support**: Allow users to configure settings through a YAML file, such as specifying regions, tags to look for, or specific organizational policies to enforce.
7. **Logging and Error Handling**: Implement logging to track the execution of the tool and provide informative error messages when issues arise.
8. **Testing**: Write unit tests to ensure each part of the tool functions correctly, especially the validation logic.

The goal is to create a robust and flexible tool that helps DevOps teams maintain best practices in their AWS LakeFormation configurations.

💬 Discussion Feed

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