arxivtd

v0.1.1 suspicious
4.0
Medium Risk

ArXiv Trust Delineation - CLI for academic paper analysis

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is suspected of potential misuse due to its low activity and lack of maintainer information, despite showing no immediate malicious activities.

  • Metadata risk indicating potential misuse
  • Lack of maintainer information
Per-check LLM notes
  • Network: The observed network calls are likely intended for API health checks and file uploads, which could be part of the legitimate functionality of a tool related to arXiv or GROBID processing.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of potential misuse due to low activity and lack of maintainer information.

📦 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 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://arxivtd.com/docs
  • Detailed PyPI description (1493 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 10 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 2 commits in dmarakom6/arXivTD
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • ) try: response = requests.get(f"{grobid_url}/api/health", timeout=5) return respon
  • i_key} response = requests.get(url, headers=headers) if response.status_code =
  • y} response = requests.post(url, files=files, headers=headers) if response.
  • y} response = requests.post(url, files=files, headers=headers, timeout=120)
  • i_key} response = requests.get(endpoint, headers=headers, timeout=30) if respo
✓ 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: arxivtd.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Very few commits: 2 total
  • Single contributor with only 2 commit(s) — possibly throwaway account
⚠ 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 arxivtd 
Create a Python-based mini-application named 'AcademicInsight' that leverages the 'arxivtd' package to analyze academic papers from the arXiv repository. The goal of 'AcademicInsight' is to provide researchers and students with insights into the trustworthiness and relevance of academic papers based on their content analysis. Here’s a detailed breakdown of the steps and features you should implement:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python 3.x installed and create a virtual environment for your project. Install the 'arxivtd' package along with other necessary libraries such as pandas for data manipulation and matplotlib for visualizations.

2. **CLI Interface**: Develop a command-line interface (CLI) using Python’s argparse module. This CLI should accept commands to search for papers by keywords, retrieve specific papers by ID, and perform trust analysis on retrieved papers.

3. **Paper Search**: Implement a feature within the CLI that allows users to search for papers based on keywords. Users should be able to input a query and receive a list of relevant papers with metadata such as title, authors, abstract, and publication date.

4. **Trust Analysis**: Utilize the 'arxivtd' package to perform a trust analysis on selected papers. This involves analyzing the content of the papers to determine their reliability and trustworthiness. Display the results in a readable format, highlighting key metrics and scores provided by 'arxivtd'.

5. **Visualization**: Integrate matplotlib to visualize the trust analysis results. Create graphs or charts that represent the trust scores of multiple papers side-by-side for easy comparison.

6. **Export Functionality**: Add functionality to export the analyzed data to a CSV file. This will allow users to save and further analyze the data outside of the application.

7. **User Guide**: Write a comprehensive user guide that explains how to use each feature of 'AcademicInsight', including examples of queries and expected outputs.

By following these steps, you will develop a powerful tool that enhances the research process by providing valuable insights into the trustworthiness and relevance of academic papers.

💬 Discussion Feed

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