aws-grobid

v0.3.0 suspicious
4.0
Medium Risk

Deploy GROBID on AWS EC2

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some level of network activity that could be for external validation purposes, but with incomplete author details and potential inactivity from the maintainer, it warrants further investigation.

  • network risk due to possible external validation
  • incomplete author details and potential inactivity from maintainer
Per-check LLM notes
  • Network: The network call pattern suggests the package may be checking for a service availability or performing some form of external validation, which is not inherently malicious but should be reviewed based on the package's documentation and intended use.
  • Shell: No shell execution patterns detected, indicating a low risk for direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author details are incomplete and the maintainer seems to be new or inactive, which raises some concern but not enough to conclusively identify it as malicious.

📦 Package Quality Overall: Medium (5.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://evamaxfield.github.io/aws-grobid
  • Detailed PyPI description (4808 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 14 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 31 commits in evamaxfield/aws-grobid
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • try: response = requests.get(alive_url, timeout=5) if response.status_code ==
✓ 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 evamaxfield/aws-grobid 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-grobid 
Create a document processing mini-app using the 'aws-grobid' Python package. This app will utilize GROBID, a powerful tool for extracting information from academic papers, deployed on AWS EC2 instances. The goal is to develop a user-friendly interface where users can upload PDF files of academic papers and receive structured data outputs such as author names, publication dates, abstracts, citations, etc.

### Key Features:
- **User Interface**: Develop a simple web-based UI using Flask or Django, allowing users to upload their PDF documents.
- **AWS EC2 Integration**: Use 'aws-grobid' to deploy GROBID on an EC2 instance and configure it to process uploaded documents.
- **Data Extraction**: Implement functionality to extract key metadata from the uploaded PDFs, including author names, titles, abstracts, and references.
- **Output Display**: Present the extracted data in a structured format on the same UI, making it easy for users to review and download the processed information.
- **Error Handling**: Ensure robust error handling for cases where the input PDF might not be compatible or if there are issues during processing.

### Steps to Build the Application:
1. Set up an AWS account and create an EC2 instance suitable for running GROBID.
2. Install and configure 'aws-grobid' on your EC2 instance following the package documentation.
3. Create a basic web application using Flask or Django, integrating file upload capabilities.
4. Connect your web app to the GROBID service running on the EC2 instance through API calls.
5. Implement data extraction logic based on GROBID's output formats and present the data in a user-friendly manner.
6. Test thoroughly, focusing on edge cases like unsupported file types or corrupted PDFs.
7. Deploy your application either locally or on a cloud platform like AWS S3 for accessibility.

This project will not only showcase your skills in deploying machine learning models on cloud infrastructure but also demonstrate practical use cases for academic research and publication management.

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