aws-resource-validator-support-app

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits minimal direct risks but concerns arise from the metadata risk due to the maintainer's new or inactive account and lack of detailed author information.

  • Low direct risk indicators (network, shell, obfuscation, credentials)
  • Metadata risk due to maintainer's account status and lack of author details
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external API interactions.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of code obfuscation for malicious purposes.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or credential theft.
  • Metadata: The maintainer has a new or inactive account with limited package history and lacks author details.

📦 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-support-app 
Create a mini-application named 'AWS Support App Validator' that leverages the 'aws-resource-validator-support-app' package to validate and manage AWS support app resources efficiently. This application should provide a user-friendly interface for developers and system administrators to ensure their AWS support applications meet specific validation criteria defined within the 'aws-resource-validator-support-app'.

Step-by-Step Instructions:
1. **Setup Project Environment**: Initialize a new Python project, install necessary dependencies including 'aws-resource-validator-support-app', and set up a virtual environment.
2. **Define Validation Rules**: Utilize the Pydantic v2 models provided by 'aws-resource-validator-support-app' to define validation rules for different AWS support app resources.
3. **Input Resource Data**: Implement functionality to accept input data for AWS support apps either via command line arguments or a simple UI. Ensure that users can specify which type of resource they want to validate (e.g., app, service, etc.).
4. **Validation Engine**: Develop a robust validation engine that applies the defined rules to the input data. Use the 'aws-resource-validator-support-app' package to perform the actual validation based on the Pydantic models.
5. **Output Results**: Display the validation results to the user, indicating whether the input resource data meets the specified criteria. Provide detailed error messages if any issues are found.
6. **Logging & Reporting**: Integrate logging to capture validation activities and errors for auditing purposes. Additionally, allow users to generate reports summarizing validation outcomes over time.
7. **Testing & Documentation**: Write comprehensive tests to verify the correctness of your validation logic and ensure the application functions as intended under various scenarios. Prepare documentation that explains how to use the application effectively.

Suggested Features:
- Support for multiple validation scenarios based on different AWS support app configurations.
- Real-time feedback during input entry to help users correct potential issues before final submission.
- Integration with AWS services for fetching live data against which the validation can be performed.
- Option to save validation configurations for reuse across different projects or environments.

💬 Discussion Feed

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