aws-resource-validator-dlm

v2.0.3 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks across all assessed categories, primarily due to its lack of network activity, shell execution, obfuscation, and credential harvesting. However, the incomplete author information slightly increases the metadata risk.

  • Low risk scores across all primary categories.
  • Incomplete author information noted.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package focused on local resource validation.
  • Shell: No shell execution patterns detected, consistent with a benign utility focused on AWS DLM resource validation.
  • 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 author seems 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 (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-dlm 
Create a Python-based CLI tool named 'DLMValidator' that leverages the 'aws-resource-validator-dlm' package to validate AWS Data Lifecycle Management (DLM) policies. This tool will help users ensure their DLM policies comply with specific criteria or best practices, enhancing the reliability and security of their AWS resources.

### Core Functionality:
1. **Policy Validation**: The tool should take a DLM policy as input (either via file upload or direct JSON input) and validate it against predefined rules. These rules could include checking if the policy includes all necessary actions, if the resource types are correctly specified, etc.
2. **Rule Customization**: Users should be able to customize validation rules based on their organizational requirements. For example, they might want to enforce certain tags or restrict operations to specific regions.
3. **Report Generation**: After validation, the tool should generate a comprehensive report detailing any issues found, suggestions for improvement, and compliance status.
4. **Integration with AWS**: The tool should integrate seamlessly with AWS services, allowing users to validate policies directly from their AWS accounts without manual intervention.
5. **User-Friendly Interface**: Ensure the CLI is intuitive and easy to use, with clear error messages and usage instructions.

### Utilizing 'aws-resource-validator-dlm':
- Use the Pydantic v2 models provided by 'aws-resource-validator-dlm' to define the structure of DLM policies and validation rules.
- Leverage these models to parse and validate DLM policies efficiently, ensuring that the tool accurately reflects the structure and content of AWS DLM policies.
- Implement custom validators based on the models to enforce additional organizational policies or best practices.

### Suggested Features:
- Support for multiple DLM policies at once.
- A web dashboard for viewing validation results and reports.
- Automated scheduling of policy validations using AWS Lambda or scheduled tasks.
- Integration with CI/CD pipelines to automatically validate DLM policies during deployment processes.

💬 Discussion Feed

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