🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits shell execution without clear context, raising suspicion about its intended use. Despite low risks in other categories, this behavior warrants further investigation.

Shell risk due to unexplained shell execution
Low metadata effort suggesting new or less experienced author

Per-check LLM notes

Network: No network calls detected.
Shell: Shell execution is present but without clear context, indicating potential risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author has only one package and lacks PyPI classifiers, indicating low effort or newness, but no clear malicious intent is signaled.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

umped input return_code = subprocess.call(command, shell=True) # nosec # Exit code -> 4C failed
log'} 2>&1" return_code = subprocess.call(command, shell=True) # nosec # Exit code -> script fai
de = subprocess.call(command, shell=True) # nosec # Exit code -> 4C failed if return_code:
de = subprocess.call(command, shell=True) # nosec # Exit code -> script failed if return_c

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository 4C-multiphysics/fourcipp appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "FourCIPP Authors" 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 FourCIPP

Your task is to develop a fully-functional mini-application named '4CAnalyzer' that leverages the capabilities of the Python package 'FourCIPP'. This tool aims to simplify the process of analyzing and manipulating 4C input files, which are commonly used in certain scientific and engineering applications. Your application will not only parse these files but also provide a user-friendly interface for viewing, modifying, and exporting data.

**Application Requirements:**
- **File Parsing:** Use 'FourCIPP' to efficiently read and parse 4C input files into a structured format (e.g., dictionaries, lists).
- **Data Visualization:** Implement a feature that allows users to visualize parsed data using matplotlib or seaborn. This could include graphs, charts, or any other relevant visual representation.
- **Modification Interface:** Provide a simple GUI or command-line interface where users can modify the parsed data directly.
- **Export Functionality:** Enable users to export modified data back into a 4C input file format, ensuring all changes are correctly saved.
- **Error Handling:** Ensure robust error handling for scenarios such as invalid file formats, missing data, etc.
- **Documentation:** Include comprehensive documentation on how to use 'FourCIPP' within your application and how to install and run the application itself.

**Utilization of 'FourCIPP':** 
- Utilize 'FourCIPP' for its core functionality of parsing 4C input files. Integrate it seamlessly into your application's workflow to ensure efficient and accurate data processing.
- Explore additional functionalities or custom parsers provided by 'FourCIPP' that might enhance your application's capabilities.
- Consider contributing any enhancements or improvements back to the 'FourCIPP' community if they add significant value.

This project is designed to demonstrate your ability to integrate third-party packages effectively, handle complex data structures, and provide a useful tool for end-users.