aws-resource-validator-textract

v2.0.3 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal signs of potential risk in terms of network calls, shell execution, and obfuscation. However, the metadata risk score is elevated due to the maintainer's new or inactive account and lack of proper author identification, raising concerns about the package's origin and legitimacy.

  • Metadata risk due to new/inactive maintainer account
  • Lack of proper author name
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services to function.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • 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 and lacks a proper author name, which raises some suspicion but not enough to conclusively identify it as malicious.

📦 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 (303 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-textract 
Create a document analysis tool using the 'aws-resource-validator-textract' Python package. This tool will allow users to upload images or PDF files containing text, and then use Amazon Textract to extract and validate the text content from these documents. Here’s a detailed step-by-step guide on how to develop this application:

1. **Setup**: Install the necessary packages including 'aws-resource-validator-textract', Boto3 (AWS SDK for Python), and any additional libraries needed for file handling.
2. **User Interface**: Develop a simple web interface using Flask where users can upload their documents. Ensure that the interface supports both image and PDF file types.
3. **Document Processing**: When a user uploads a document, the application should use the 'aws-resource-validator-textract' package to define the structure of the expected data from Textract responses. This will help in validating the extracted data against predefined schemas.
4. **Text Extraction**: Use Amazon Textract to process the uploaded document and extract text, tables, and other relevant information.
5. **Data Validation**: After extracting the data, validate it against the defined schemas using the models provided by 'aws-resource-validator-textract'. This ensures the integrity and correctness of the extracted data.
6. **Result Presentation**: Display the validated results back to the user in a structured format, highlighting any issues found during validation.
7. **Error Handling**: Implement robust error handling mechanisms to manage cases where the document might not be readable or when there are issues with the AWS service.
8. **Security Measures**: Ensure that all sensitive data is handled securely and that the application complies with AWS security best practices.
9. **Testing**: Conduct thorough testing to ensure the application works correctly under various conditions and handles different types of input files effectively.

**Suggested Features**:
- Support for multiple languages in text extraction.
- Ability to save and export the validated output.
- Integration with a database to store processed documents and their validation results for future reference.
- User authentication to secure access to the application.

This project will showcase the power of 'aws-resource-validator-textract' in simplifying the process of working with complex AWS services like Textract, while also providing a practical solution for document analysis needs.

💬 Discussion Feed

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