AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in network and shell activities, but its use of obfuscated code and low metadata activity raise concerns about potential malicious intent or supply-chain attack.
- Unconventional syntax suggesting possible obfuscation
- Low metadata activity and a non-secure link
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution patterns detected, indicating no immediate risk of executing system commands.
- Obfuscation: The code uses unconventional syntax which may indicate an attempt to obscure the logic from casual inspection.
- Credentials: No patterns indicative of credential harvesting were found in the provided snippet.
- Metadata: Low activity and a non-secure link suggest caution, but no clear signs of malicious intent.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
comp) if not __import__('networkx').is_connected(subG): continue
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: miaz.ch>
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://creativecommons.org/licenses/by-nd/4.0/
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 2.0
1 maintainer concern(s) found
Author "Luc T. Miaz" 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 PFASGroups
Develop a desktop application using Python that leverages the 'PFASGroups' package to analyze chemical compounds and classify them based on their PFAS group characteristics. This application should allow users to input SMILES strings or upload molecule files (such as .sdf or .mol) and receive a detailed report on the detected PFAS groups within the molecule. Here’s a step-by-step guide on how to build this application: 1. **Setup Environment**: Install Python and necessary libraries including PFASGroups, RDKit for molecule handling, and PyQt5 for the GUI. 2. **Design the User Interface**: Create a simple yet effective GUI where users can either type in a SMILES string or browse and select a file to upload. Include buttons for initiating the analysis and displaying results. 3. **Molecule Input Handling**: Implement functionality to read in molecules from SMILES strings or uploaded files. Ensure the application supports common chemical file formats like .sdf and .mol. 4. **Analysis Module**: Utilize PFASGroups to analyze the inputted molecules. Use the package’s capabilities to detect and classify all relevant PFAS groups present in the molecule. Highlight any potential issues or unique characteristics of the molecule based on its PFAS content. 5. **Results Display**: Design a clean interface to display the analysis results. Include a summary of the detected PFAS groups, their properties, and any additional insights provided by PFASGroups such as molecular embedding creation for further machine learning applications. 6. **Export Results**: Allow users to export the analysis results into a user-friendly format like PDF or Excel for record-keeping or sharing. 7. **Documentation and Testing**: Write clear documentation explaining how to use the application and what each feature does. Conduct thorough testing to ensure accuracy and reliability of the analysis module. This project aims to bridge the gap between advanced cheminformatics tools and practical usability for chemists and researchers working with PFAS.