AI Analysis
Final verdict: SUSPICIOUS
The package shows minimal risk indicators such as no network calls, shell executions, or obfuscation. However, the metadata risk due to sparse author information and potentially inactive account raises suspicion.
- Sparse author information
- 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 no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author's information is sparse and the account seems new or inactive, raising some concerns but not definitive indicators 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 (294 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_validatorSmall 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 shortAuthor "" 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-mturk
Create a Python-based utility called 'MTurkTaskValidator' that leverages the 'aws-resource-validator-mturk' package to validate MTurk resources according to AWS specifications. This tool should help developers and system administrators ensure that their MTurk tasks meet the necessary criteria before deployment. The application will include a command-line interface (CLI) for ease of use and integration into existing workflows. Key Features: - Validate MTurk HITs (Human Intelligence Tasks) against predefined schemas using Pydantic models from the 'aws-resource-validator-mturk' package. - Support batch validation for multiple HITs at once, allowing users to upload a JSON file containing HIT definitions. - Provide detailed error messages indicating which fields or properties do not conform to the expected AWS standards. - Allow customization of validation rules through configuration files, enabling users to define specific requirements for different types of HITs. - Offer an option to automatically correct minor issues (such as missing fields) if possible, based on common conventions. - Integrate with logging mechanisms to track validation activities and errors for auditing purposes. Steps to Implement: 1. Set up the project structure including directories for source code, tests, and documentation. 2. Install the 'aws-resource-validator-mturk' package and other necessary dependencies like Pydantic, Click (for CLI), and logging libraries. 3. Define the CLI structure using Click, incorporating commands for validating single HITs, batch validating HITs, and configuring validation rules. 4. Utilize Pydantic models from 'aws-resource-validator-mturk' to create validators for different types of MTurk resources. 5. Implement logic for reading and validating HIT definitions from JSON files, handling both single and batch validations. 6. Develop error-handling mechanisms to provide clear feedback on validation failures. 7. Add functionality for automatic correction of simple validation issues where applicable. 8. Configure logging to capture validation processes and errors for future reference. 9. Write unit tests for each feature to ensure reliability and maintainability of the tool. 10. Document the usage instructions, including examples of valid and invalid HIT definitions, and how to customize validation rules. By following these steps and incorporating the specified features, you'll develop a robust utility that significantly enhances the quality assurance process for MTurk tasks.
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