🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation risk due to the use of eval with dynamic input, which is suspicious behavior often associated with attempts to conceal malicious activities. While there's no direct evidence of harmful intent, the overall risk is elevated.

High obfuscation risk
No description provided

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell executions detected, indicating no immediate risk from command execution.
Obfuscation: The use of eval with dynamic input is highly suspicious and suggests an attempt to hide the true functionality of the code.
Credentials: No direct evidence of credential harvesting was found, but caution is advised as obfuscation techniques may mask such activities.
Metadata: The package shows signs of low activity and possibly low maintainer experience, raising suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

correct=eval(judge,namespace) except:
script=eval(args.script,namespace) else:

✓ 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: yeah.net

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Enbuging" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with VBA-Python-Loader

Create a financial analysis tool using Excel and Python integration via the 'VBA-Python-Loader' package. This tool will allow users to perform complex financial calculations and analyses directly within their Excel environment without needing to leave the spreadsheet interface. Here are the key steps and features you'll implement:

1. **Setup Integration**: First, ensure you have the 'VBA-Python-Loader' package installed and correctly configured in your Excel workbook. This package allows you to call Python scripts from VBA macros.

2. **Data Input**: Design a user-friendly Excel interface where users can input financial data such as stock prices, interest rates, and other relevant economic indicators. This data will serve as inputs for your financial models.

3. **Python Script Execution**: Write Python scripts that perform various financial analyses, such as calculating compound interest, evaluating investment returns, or forecasting future stock prices based on historical data. Use these scripts to demonstrate how 'VBA-Python-Loader' enables seamless execution of Python code from VBA.

4. **Result Display**: After executing the Python scripts, display the results back in Excel. For example, show calculated interest amounts, investment return percentages, or forecasted stock price trends in dedicated cells or charts within the workbook.

5. **Interactive Features**: Implement interactive features like dropdown menus or buttons in Excel that allow users to select different types of financial analyses to run, dynamically adjusting the Python scripts being executed.

6. **Documentation and User Guide**: Provide clear documentation explaining how each feature works and how users can modify the scripts for their specific needs. Include a guide on how to set up the 'VBA-Python-Loader' package if it's not already installed.

This project will showcase the power of integrating Python with Excel through VBA, providing a practical example of how developers and analysts can leverage both tools together for enhanced productivity and analysis capabilities.