🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of obfuscation that could be used for malicious purposes, coupled with a lack of detailed metadata from a potentially inactive maintainer.

High obfuscation risk due to eval and random imports
Low metadata credibility due to new/inactive maintainer

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no direct system command execution risk.
Obfuscation: The presence of eval and random imports within an obfuscated context suggests potential for code injection or evasion techniques.
Credentials: No clear patterns indicative of credential harvesting were detected.
Metadata: The maintainer has a new or inactive account and lacks detailed author information, which raises some suspicion but does not definitively indicate malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

stop": print(eval(line)) import random #(19.03.2026) def Drift(): #(19.03

✓ 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: yandex.ru>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository HubBase-Authority/HubBasePE 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 HubBasePE

Create a comprehensive Python mini-application named 'DataHubManager' that leverages the capabilities of the 'HubBasePE' package to manage, analyze, and visualize datasets from various sources. The application should provide functionalities such as importing data from CSV files, databases, and APIs, performing basic statistical analysis, generating visualizations, and exporting results back to file formats like CSV or Excel.

### Key Features:
1. **Data Import**: Implement a feature to import datasets from local CSV files, SQL databases, and RESTful APIs. Utilize 'HubBasePE' to handle different data sources seamlessly.
2. **Statistical Analysis**: Develop functions within your app to calculate basic statistics such as mean, median, mode, standard deviation, and correlation coefficients using 'HubBasePE'.
3. **Data Visualization**: Integrate 'HubBasePE' to create visual representations of the imported data through graphs and charts, including line plots, bar charts, scatter plots, and histograms.
4. **Exporting Results**: Allow users to export the analyzed data and visualizations into different formats like CSV, Excel, and image files (PNG, JPEG).
5. **User Interface**: Optionally, develop a simple command-line interface (CLI) or a basic GUI using libraries like Tkinter or PyQt to make the application more user-friendly.
6. **Error Handling**: Ensure robust error handling throughout the application to manage issues like invalid file paths, database connection errors, and API request failures gracefully.
7. **Documentation**: Provide clear documentation on how to install the application, run it, and use its features effectively.

### Steps to Build the Application:
1. **Setup Environment**: Install necessary packages including 'HubBasePE', pandas, matplotlib, seaborn, and any other required libraries.
2. **Data Import Module**: Create a module to handle data imports from CSVs, databases, and APIs using 'HubBasePE'.
3. **Analysis Module**: Develop another module for statistical analysis where 'HubBasePE' functions will be utilized to perform calculations.
4. **Visualization Module**: Use 'HubBasePE' to generate visual representations of the data. This module should be capable of producing multiple types of plots.
5. **Export Module**: Implement functionality to save the processed data and visualizations in specified formats.
6. **Interface Development**: If choosing to develop a GUI, design it to interact with the above modules efficiently.
7. **Testing and Debugging**: Thoroughly test each component of the application for accuracy and reliability. Fix any bugs encountered during testing.
8. **Final Documentation**: Write detailed documentation explaining every aspect of the application including setup instructions, usage guidelines, and troubleshooting tips.

By following these steps and utilizing the 'HubBasePE' package effectively, you'll create a versatile tool for managing and analyzing data efficiently.