aws-resource-validator-amp

v2.0.3 safe
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risk indicators, with no network calls, shell executions, obfuscations, or credential harvesting detected. The metadata risk is slightly elevated due to incomplete author details and a new/inactive user status.

  • No network calls detected
  • Incomplete author details
  • New or inactive user
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 no immediate risk of unauthorized system command execution.
  • 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 details are incomplete, and they seem to be a new or inactive user with only one package.

📦 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 (288 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-amp 
Create a Python-based utility named 'AWS Resource Auditor' that leverages the 'aws-resource-validator-amp' package to validate and audit AWS resources against predefined schemas. This tool will help developers and DevOps engineers ensure their AWS resource configurations comply with best practices and organizational policies. Here's a step-by-step guide on what your application should accomplish:

1. **Setup Environment**: Start by setting up a virtual environment and installing the necessary dependencies including 'aws-resource-validator-amp', Boto3 for AWS SDK, and Pydantic for data validation.

2. **Define Schemas**: Utilize the 'aws-resource-validator-amp' package to define schemas for different AWS services like S3 buckets, EC2 instances, RDS databases, etc. These schemas should include mandatory fields, default values, and constraints to enforce security and compliance standards.

3. **Resource Fetching**: Implement a feature that fetches live AWS resource details using Boto3 based on user input or configuration files. Users should be able to specify which AWS account, region, and service they want to audit.

4. **Validation Engine**: Develop a robust validation engine that takes fetched resource details and validates them against the defined schemas. The validation process should check if all required fields are present, if optional fields have valid data, and if any custom constraints are met.

5. **Report Generation**: Create a module that generates detailed reports based on the validation results. Reports should highlight any discrepancies between actual resource configurations and the defined schemas, along with recommendations for remediation.

6. **CLI Interface**: Design a command-line interface (CLI) that allows users to easily interact with the 'AWS Resource Auditor'. CLI commands should include options for specifying AWS credentials, selecting resources to audit, choosing validation schemas, and outputting reports in various formats like JSON or CSV.

7. **Integration Testing**: Finally, write integration tests to ensure your application works seamlessly across different scenarios. Test cases should cover various AWS services, different regions, and edge cases where resources might not conform to the schemas.

The 'aws-resource-validator-amp' package plays a crucial role in this project by providing pre-defined Pydantic models for AWS resources, streamlining the schema definition process, and ensuring data consistency and integrity during validation.

💬 Discussion Feed

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