aws-resource-validator-lex-runtime

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious activity, but the metadata risk due to the maintainer's new or inactive account and lack of author details raises concerns about its origin and intent.

  • No network calls detected
  • Maintainer has a new or inactive account
  • Lack of author details
Per-check LLM notes
  • Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity unless the package's purpose requires it.
  • Shell: No shell execution patterns detected, aligning with a typical benign package behavior.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
  • Metadata: The maintainer has a new or inactive account and lacks author details, 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 (312 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-lex-runtime 
Create a command-line utility called 'LexBotChecker' that helps developers validate their Amazon Lex bots against a set of predefined rules using the 'aws-resource-validator-lex-runtime' Python package. This tool will allow users to input the name of their Lex bot and receive a report on whether it adheres to best practices and common pitfalls. Here’s how you can structure the project:

1. **Setup**: Begin by installing the necessary packages including 'aws-resource-validator-lex-runtime'. Ensure your environment is configured with AWS credentials.
2. **Input Handling**: Design a function that accepts the name of a Lex bot as input from the user via command line arguments.
3. **Validation Logic**: Utilize the models provided by 'aws-resource-validator-lex-runtime' to fetch details about the specified Lex bot from AWS and apply validation rules. These rules could include checking if all intents have at least one sample utterance, ensuring slot types are correctly defined, and verifying that there are no duplicate utterances across intents.
4. **Reporting**: Develop a reporting mechanism that outputs the validation results in a clear, user-friendly format. This could be a simple console output or a more complex HTML report.
5. **Error Handling**: Implement robust error handling to manage cases where the bot name is incorrect, the AWS service is unreachable, or other potential issues arise during execution.
6. **Testing**: Write unit tests to ensure each component of your application works as expected. Use mock data to simulate different scenarios for testing purposes.
7. **Documentation**: Provide comprehensive documentation detailing how to install, configure, and use 'LexBotChecker', along with examples of common validation failures and how to resolve them.
8. **Optional Features**: Consider adding optional features such as the ability to specify which validation rules to run, support for multiple bot names at once, and integration with CI/CD pipelines for automated checks.

By following these steps, you'll create a valuable tool that can help maintain high standards in Amazon Lex bot development, leveraging the powerful validation capabilities offered by 'aws-resource-validator-lex-runtime'.

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