aws-resource-validator-codepipeline

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risks in terms of network, shell, obfuscation, and credential handling. However, the maintainer's new and inactive account with no author name provided raises concerns about potential supply-chain attacks.

  • New or inactive maintainer account
  • No author name provided
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package that does not require external API interactions.
  • Shell: No shell execution patterns detected, which is expected as Python packages typically do not execute shell commands unless explicitly designed to do so.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account with minimal package history and no author name provided, raising some suspicion but not conclusive evidence of malintent.

📦 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 (315 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-codepipeline 
Create a Python-based utility named 'CodePipelineValidator' that leverages the 'aws-resource-validator-codepipeline' package to validate AWS CodePipeline configurations before they are deployed. This utility will serve as a pre-deployment check to ensure that the provided configuration adheres to best practices and meets all necessary requirements, thereby reducing the likelihood of deployment failures due to misconfigurations.

### Project Requirements:
1. **Configuration Parsing**: The utility should accept a JSON or YAML file representing an AWS CodePipeline configuration. This file can be passed via command line arguments or read from a specified path.
2. **Validation Logic**: Utilize the 'aws-resource-validator-codepipeline' package to define validation rules based on Pydantic models. These rules should cover various aspects such as pipeline structure, stage definitions, action types, and any other relevant parameters.
3. **Error Reporting**: If the configuration fails validation, the utility should output a detailed report highlighting the issues found. This report should include error codes, descriptions, and suggestions for fixing the errors.
4. **Success Confirmation**: Upon successful validation, the utility should confirm that the configuration is valid and ready for deployment.
5. **Integration with CI/CD**: Consider integrating the utility into a CI/CD pipeline so that it runs automatically whenever changes are made to the pipeline configuration.
6. **User-Friendly Interface**: Provide a user-friendly CLI interface where users can easily input the path to their configuration file and receive feedback.
7. **Documentation**: Include comprehensive documentation that explains how to use the utility, the structure of the validation rules, and how to interpret the validation reports.
8. **Extensibility**: Design the utility in a way that allows for easy addition of new validation rules without requiring significant changes to the existing codebase.

### Example Workflow:
- User specifies a path to a JSON/YAML file containing an AWS CodePipeline configuration using the CLI.
- The utility reads the configuration file and uses the 'aws-resource-validator-codepipeline' package to validate the contents against predefined rules.
- If the configuration passes validation, the utility outputs a confirmation message indicating that the configuration is valid.
- If the configuration fails validation, the utility generates a detailed error report listing all issues found and provides guidance on how to correct them.

By building this utility, you'll create a valuable tool for developers and DevOps engineers looking to ensure their AWS CodePipeline configurations are robust and free of common pitfalls.

💬 Discussion Feed

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