aws-resource-validator-cleanroomsml

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risks in terms of network usage, shell execution, obfuscation, and credential harvesting. However, the incomplete metadata and potential inactivity of the maintainer raise concerns that could indicate a less trustworthy source.

  • Incomplete maintainer metadata
  • Potential inactivity of the maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of code being hidden for malicious purposes.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or sensitive information being stolen.
  • Metadata: The maintainer's author name is missing or very short and seems to be new or inactive, which raises some suspicion but not enough to conclusively identify 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 (315 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-cleanroomsml 
Create a command-line tool using Python that helps developers validate their AWS CleanRoomsML resources before deployment. This tool should leverage the 'aws-resource-validator-cleanroomsml' package, which provides Pydantic v2 models specifically designed for validating AWS CleanRoomsML resources. The application should follow these steps and include the suggested features:

1. **Initialization**: Start by importing necessary modules from 'aws-resource-validator-cleanroomsml' and setting up the basic structure of the CLI tool.
2. **Input Handling**: Allow users to input or load resource configurations either via command line arguments or from a file. Ensure that the input can be in YAML or JSON format, supporting both local files and URLs.
3. **Validation Logic**: Implement validation logic using the Pydantic models provided by 'aws-resource-validator-cleanroomsml'. This includes checking the structure, types, and completeness of the resource configurations against the defined schemas.
4. **Output Reporting**: Once validation is complete, provide a detailed report on the status of each resource configuration. Highlight any errors or warnings found during the validation process.
5. **Customization Options**: Offer customization options such as specifying validation rules, ignoring certain fields, or custom error messages.
6. **Integration with AWS**: For advanced usage, integrate the tool with AWS services to fetch existing resources and compare them against the validated configurations, ensuring consistency between the current state and proposed changes.
7. **Documentation and Help**: Provide comprehensive documentation and a help menu within the CLI tool to guide users through the setup and usage of the tool.

This project aims to streamline the validation process for AWS CleanRoomsML resources, reducing the likelihood of deployment issues due to misconfigured resources. By utilizing 'aws-resource-validator-cleanroomsml', you ensure that your application adheres to best practices and standards set by AWS.

💬 Discussion Feed

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