NepTrainKit

v2.8.4 suspicious
6.0
Medium Risk

NepTrainKit is a Python package for visualizing and manipulating training datasets for NEP.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package uses potentially risky practices such as shell execution and obfuscated code, raising concerns about its legitimacy and potential for misuse.

  • High obfuscation risk due to the use of eval with restricted builtins.
  • Moderate shell risk indicating possible execution of external commands.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package is expected to perform network operations.
  • Shell: Shell execution suggests the package may run external commands, which could be legitimate but should be reviewed for potential misuse or unintended consequences.
  • Obfuscation: The use of eval with restricted builtins may indicate an attempt to bypass security measures, suggesting potential malicious intent.
  • Credentials: No direct patterns for harvesting credentials or secrets were identified.
  • Metadata: The author has only one package, which may indicate a new or less active account, but no other red flags were identified.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • v["e"] = math.e val = eval(code, {"__builtins__": {}}, env) # noqa: S307 val =
  • env["e"] = math.e out = eval(code, {"__builtins__": {}}, env) # noqa: S307 out_arr =
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • try: subprocess.check_call(cmd) except Exception as e: prin
  • join(cmd)}") result = subprocess.run(cmd, cwd=cwd, env=env) if result.returncode != 0:
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: qq.com>

  • Very short email domain: qq.com>
✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aboys-cb/NepTrainKit appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Chen Cheng bing" 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 NepTrainKit 
Create a mini-application named 'NEP Trainer Visualizer' that leverages the capabilities of the 'NepTrainKit' Python package. This application will serve as a tool for researchers and data scientists working with Non-Equilibrium Path Sampling (NEP) methods. The goal of this application is to provide a user-friendly interface for visualizing and manipulating training datasets specific to NEP simulations. Here’s a step-by-step guide on what your application should accomplish:

1. **Setup Environment**: Ensure that the environment includes all necessary libraries including 'NepTrainKit'. Provide instructions on how to install these dependencies.
2. **Data Importation**: Allow users to import their own datasets compatible with NEP training requirements. Implement error handling to ensure the dataset meets the required format and dimensions.
3. **Visualization Tools**: Utilize 'NepTrainKit' to visualize the imported dataset in various ways (e.g., scatter plots, histograms). Each visualization should include interactive elements such as tooltips displaying additional information about the data points when hovered over.
4. **Manipulation Features**: Enable users to manipulate the dataset through features like filtering data based on specific criteria, adding or removing data points, and adjusting parameters that affect the NEP training process.
5. **Export Options**: Offer options for exporting the manipulated dataset back into formats suitable for further analysis or use in NEP simulations.
6. **User Interface**: Design an intuitive graphical user interface using frameworks like PyQt or Tkinter. The UI should be responsive and easy to navigate, guiding users through each step of the process from importing to exporting their datasets.
7. **Documentation**: Write comprehensive documentation detailing how to use each feature of the application, along with examples and best practices for utilizing 'NepTrainKit' functionalities effectively.

Suggested Features:
- Integration with common data science tools like Jupyter Notebooks for seamless workflow integration.
- Real-time feedback during data manipulation to help users understand the impact of their changes.
- Advanced analytics tools that leverage 'NepTrainKit' to provide deeper insights into the dataset, such as identifying outliers or patterns relevant to NEP training.
- Support for multiple file formats to accommodate different data sources.

By following these guidelines, you'll create a powerful yet accessible tool that enhances the NEP research community's ability to work with complex training datasets.