aws-resource-validator-bedrock

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk in terms of direct threats like network calls, shell executions, and obfuscation techniques. However, the metadata risk score is elevated due to sparse author information and potentially inactive author account, which raises suspicion about its legitimacy.

  • Metadata risk score is 3 out of 10
  • Author information is sparse and the account may be new or inactive
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 the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting legitimate usage.
  • Metadata: The author information is 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-bedrock 
Create a Python-based command-line tool named 'BedrockValidatorCLI' that leverages the 'aws-resource-validator-bedrock' package to validate AWS Bedrock resources against their respective schemas. This tool should enable users to input resource configurations in YAML format and receive validation feedback indicating whether the configuration adheres to AWS Bedrock standards.

### Steps to Implement:
1. **Setup Project**: Initialize a new Python project with necessary dependencies including 'aws-resource-validator-bedrock', 'pyyaml' for YAML parsing, and 'boto3' for AWS interaction if needed.
2. **Define Input Format**: Design a YAML schema that users can follow to structure their AWS Bedrock resource configurations. This schema should include placeholders for required fields, optional fields, and any specific constraints.
3. **Resource Validation**: Utilize the Pydantic models provided by 'aws-resource-validator-bedrock' to validate the user-provided YAML configurations. Ensure that the validation process checks for compliance with AWS Bedrock standards and returns meaningful error messages for any discrepancies.
4. **Command-Line Interface**: Develop a simple yet effective CLI using Python's built-in argparse module. The CLI should accept a YAML file as input, validate it against the AWS Bedrock schema, and output the validation results to the console.
5. **Testing**: Implement unit tests to ensure that your tool correctly validates various resource configurations, including edge cases and common errors.
6. **Documentation**: Provide clear documentation on how to use the tool, including examples of valid and invalid YAML configurations, and explanations of the validation process.

### Suggested Features:
- **Interactive Mode**: Allow users to enter resource configurations directly into the CLI without needing to save them to a file first.
- **Detailed Error Reporting**: Beyond just indicating whether a configuration is valid or not, provide detailed reports on which fields caused issues and why.
- **Integration with CI/CD Pipelines**: Offer an option for integrating the validation process into CI/CD pipelines to automatically check resource configurations before deployment.
- **Customizable Schema Support**: Enable users to define their own schemas based on custom requirements, allowing for more flexibility in validation criteria.

💬 Discussion Feed

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