ArrayViewer

v1.1.5 suspicious
6.0
Medium Risk

ArrayViewer

🤖 AI Analysis

Final verdict: SUSPICIOUS

The ArrayViewer package exhibits significant obfuscation and shell execution risks, indicating potential for malicious activity despite no explicit evidence of credential theft or network abuse.

  • High obfuscation risk due to the use of 'eval' with a modified global namespace.
  • Significant shell risk from executing commands without proper sanitization.
Per-check LLM notes
  • Network: The network call is to fetch metadata from PyPI, which seems legitimate.
  • Shell: Executing shell commands can be risky if not properly sanitized; this could potentially lead to unauthorized actions.
  • Obfuscation: The use of 'eval' with a modified global namespace suggests an attempt to execute arbitrary code, which is highly suspicious and potentially malicious.
  • Credentials: No direct signs of credential harvesting were found, but the overall suspicious nature of the code increases the likelihood of potential hidden threats.
  • Metadata: The author information is incomplete and the maintainer has only one package, which could indicate a less experienced or potentially suspicious user.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • self.data[var] = eval(value, {"__buildins__": None}, methods) except Excep
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • he ArrayViewer.""" proc = subprocess.run( ["curl", "https://pypi.org/pypi/arrayviewer/json"],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: mail.de>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository alexschw/ArrayViewer 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 ArrayViewer 
Develop a fully-functional mini-application called 'DataExplorer' that leverages the ArrayViewer Python package to visualize and manipulate multi-dimensional arrays. This application should provide users with an intuitive interface to load, view, and interact with various types of array data, such as numerical arrays, images, and other structured data sets. The goal is to create a tool that simplifies the process of exploring complex data arrays for both educational and professional purposes.

Key Features:
1. **Array Loading**: Users should be able to import multi-dimensional arrays from common file formats like CSV, JSON, or image files.
2. **Visualization Tools**: Implement features within ArrayViewer to display the imported arrays in different ways, including but not limited to line plots, scatter plots, heatmaps, and histograms.
3. **Interactive Exploration**: Allow users to zoom in/out, pan across the data, and select specific regions of interest for closer inspection.
4. **Data Manipulation**: Enable basic operations on the arrays, such as filtering, slicing, and applying mathematical transformations.
5. **Export Functionality**: Provide options to export the visualized data back into file formats or directly into a clipboard for further analysis or reporting.
6. **Customization Options**: Allow customization of plot settings such as color schemes, axis labels, and titles to better suit user needs.
7. **Help and Documentation**: Include comprehensive help sections and tooltips within the application to guide new users through its functionalities.

How to Utilize ArrayViewer Package:
- Import the necessary modules from ArrayViewer at the beginning of your Python script or Jupyter notebook.
- Use ArrayViewer's functions to load your dataset into memory.
- Leverage ArrayViewer's visualization capabilities to render the data in a meaningful way.
- Implement event listeners or command-line inputs to enable interactive exploration of the data.
- Integrate ArrayViewer's manipulation tools to perform operations on the loaded datasets.
- Finally, use ArrayViewer's export methods to save the modified data or visualizations for future reference.

This project aims to demonstrate the versatility and power of ArrayViewer in handling complex data visualization tasks efficiently.