AI Analysis
Final verdict: SUSPICIOUS
The package shows signs of potential obfuscation that could be used to hide malicious activity, combined with an incomplete and possibly inactive maintainer profile.
- High obfuscation risk
- Incomplete maintainer profile
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external communication.
- Shell: No shell execution patterns detected, indicating no direct system command execution.
- Obfuscation: The obfuscation pattern suggests an attempt to hide code execution details, which could indicate malicious intent or an effort to avoid detection.
- Credentials: No clear patterns of credential harvesting were detected, suggesting a low risk of direct credential theft.
- Metadata: The maintainer has an incomplete profile and seems new or inactive, raising some suspicion but not conclusive evidence of malice.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
ile in defs_files: defs = __import__(defs_file, fromlist=['']) if hasattr(defs, "fragment_defs"): fragment_de
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: hotmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository MikkelDA/COBY appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 COBY
Create a user-friendly desktop application named 'MolSysBuilder' using Python and the COBY package. This application will allow users to build coarse-grained molecular systems from scratch or import existing molecular structures in various formats (e.g., PDB, XYZ). Here’s a detailed breakdown of the steps and features your application should include: 1. **User Interface Design**: Develop an intuitive graphical interface using Tkinter or PyQt. The UI should have sections for file input/output, parameter settings, and visualization. 2. **File Import/Export**: Implement functionality to import molecular structures from common file formats like PDB, XYZ, etc. Additionally, provide options to export the generated coarse-grained models back into these formats. 3. **Parameter Configuration**: Allow users to customize parameters such as bead types, force fields, and bond definitions directly within the application. Provide default configurations based on typical coarse-graining practices. 4. **Coarse-Graining Process**: Utilize the COBY package to perform the actual coarse-graining process. Integrate COBY's functions to define beads, apply force fields, and generate the final coarse-grained model. 5. **Visualization Tool**: Incorporate a simple visualization tool within the application to display the original and coarse-grained molecular structures side-by-side. Use libraries like Matplotlib or PyMOL for this purpose. 6. **Documentation and Help**: Include a comprehensive help section that explains the basics of coarse-graining, the functionalities of the application, and how to use COBY effectively. 7. **Testing and Validation**: Ensure thorough testing of the application with different molecular structures and parameter sets. Validate the output against known coarse-grained models if possible. Your task is to write the necessary Python code to implement each feature, ensuring that the COBY package is utilized efficiently throughout the coarse-graining process. Document your code clearly and provide instructions on setting up and running the application.