aws-resource-validator-glacier

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal signs of malicious activity, but the sparse author details and potentially inactive account raise concerns about its origin and intent.

  • Sparse author details
  • Potentially inactive account
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 likely does not execute system commands.
  • 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 details are sparse and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 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 (300 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-glacier 
Create a command-line utility called 'GlacierBackupAnalyzer' using Python that leverages the 'aws-resource-validator-glacier' package to validate and analyze Amazon Glacier backup resources. This utility will help users manage their backups more efficiently by providing insights into the health and status of their Glacier vaults and archives.

The application should have the following functionalities:
1. **Vault Validation**: Validate the existence and configuration of Glacier vaults based on provided Pydantic models from the 'aws-resource-validator-glacier' package.
2. **Archive Status Check**: Fetch and display the status of archives within a specified vault, ensuring that all critical fields align with the models defined in the package.
3. **Health Report Generation**: Generate a comprehensive report detailing the health of the vaults and archives, highlighting any discrepancies or issues detected during validation and status checks.
4. **Interactive Mode**: Allow users to interactively explore specific vaults or archives, providing detailed information about each resource.
5. **Configuration Management**: Users should be able to configure their AWS credentials and preferred vaults/archives through a simple configuration file.
6. **CLI Integration**: Ensure that the application integrates seamlessly with standard CLI practices, offering clear usage instructions and error handling.

Utilize the 'aws-resource-validator-glacier' package extensively throughout the application to ensure data integrity and consistency with AWS standards. For instance, when validating vaults or checking archive statuses, use the provided Pydantic models to parse and verify the data against AWS specifications.

💬 Discussion Feed

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