Hive-ML

v2.0 suspicious
4.0
Medium Risk

Python package to run Machine Learning Experiments, within the Hive Framework.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential misuse of shell execution commands and the lack of a public repository, which raises concerns about its origin and maintenance.

  • Shell risk at 3/10 due to unverified shell execution patterns
  • Repository not found, raising suspicion about the maintainer's credibility
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: The observed shell execution pattern may be for version control operations but could warrant further investigation to ensure it's not being misused.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package and the repository is not found, which raises some suspicion but does not definitively indicate malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • ust git process = subprocess.Popen([command] + args, cwd=cwd, env=env,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: kth.se

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Bendazzoli Simone" 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 Hive-ML 
Your task is to develop a simple yet powerful application using the Hive-ML Python package. This application will serve as a basic tool for experimenting with different machine learning models on a dataset of your choice. The goal is to showcase the capabilities of Hive-ML in managing and executing machine learning experiments efficiently.

### Project Overview:
Create a web-based application where users can upload datasets, select from a variety of machine learning models, and configure experiment parameters. The application should then use Hive-ML to run these experiments and display the results.

### Key Features:
1. **User Interface**: A clean and intuitive user interface allowing users to interact with the application easily.
2. **Dataset Upload**: Users should be able to upload their own datasets in CSV format.
3. **Model Selection**: Provide a selection of machine learning models such as Linear Regression, Decision Trees, Random Forests, etc.
4. **Experiment Configuration**: Allow users to configure experiment settings like splitting data into training and testing sets, choosing evaluation metrics, etc.
5. **Experiment Execution**: Use Hive-ML to execute the selected machine learning experiments based on the user’s configurations.
6. **Results Visualization**: Display the outcomes of the experiments in a clear and understandable manner, possibly including visual charts and graphs.
7. **Documentation**: Include comprehensive documentation explaining how to use the application and how it leverages Hive-ML.

### How to Utilize Hive-ML:
- **Installation**: Ensure Hive-ML is installed and properly configured in your environment.
- **Data Preparation**: Use Hive-ML’s functionalities to prepare and preprocess the uploaded datasets.
- **Experiment Management**: Leverage Hive-ML’s capabilities to manage and run multiple machine learning experiments efficiently.
- **Result Analysis**: Use Hive-ML’s analysis tools to interpret the results of the experiments and generate insights.

### Development Steps:
1. Set up a development environment suitable for building web applications (e.g., Flask or Django).
2. Integrate Hive-ML into your project to handle machine learning tasks.
3. Design and implement the user interface for uploading datasets and configuring experiments.
4. Implement the backend logic to process the uploaded datasets and execute machine learning experiments using Hive-ML.
5. Develop the functionality to visualize and present the experiment results.
6. Test the application thoroughly to ensure all features work as expected.
7. Write documentation to guide users through the application and explain the integration of Hive-ML.

By completing this project, you will gain hands-on experience with Hive-ML and demonstrate its potential in simplifying machine learning experimentation.