aws-resource-validator-m2

v2.0.3 suspicious
4.0
Medium Risk

Pydantic v2 models for AWS m2, shipped as a PEP 420 namespace extension of aws-resource-validator.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk indicators such as no network calls, shell executions, or obfuscations. However, the incomplete metadata and possible inactivity of the author increase the suspicion level, warranting further investigation.

  • Incomplete author metadata
  • Possibly inactive author
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no risk of unauthorized credential access.
  • Metadata: The author's information is incomplete and they may be new or inactive, which raises some suspicion but not enough to conclusively indicate 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 (285 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-m2 
Create a Python-based CLI tool named 'AWS Resource Validator' using the 'aws-resource-validator-m2' package. This tool will help users validate their AWS resource configurations against predefined Pydantic v2 models provided by the package. Your goal is to ensure that any user-defined AWS resource configuration adheres to best practices and is syntactically correct before deploying it into their AWS environment.

Steps to complete this project:
1. Install the necessary packages including 'aws-resource-validator-m2', 'typer' for building the CLI, and 'pydantic' for model validation.
2. Define a set of command-line arguments that allow users to specify the type of AWS resource they want to validate (e.g., EC2 instance, S3 bucket).
3. Use the Pydantic v2 models from 'aws-resource-validator-m2' to create a function that takes in the user's configuration file (JSON or YAML format) and validates it according to the selected resource type.
4. Implement error handling to provide meaningful feedback when the validation fails, indicating which fields are missing or incorrectly formatted.
5. Add an option for users to automatically generate sample configurations based on the selected resource type if they need a starting point.
6. Ensure your CLI tool is well-documented with usage examples and explanations of common errors.

Suggested Features:
- Support for multiple AWS resource types (EC2, S3, RDS, etc.).
- Option to output validation results in JSON format for further processing.
- Integration with AWS SDK for Python (boto3) to fetch default values for optional fields.
- Ability to validate against custom Pydantic models defined by the user.
- Detailed logging of validation process and errors for troubleshooting.

💬 Discussion Feed

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