🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation through the use of eval and cloning a Git repository during installation, which raises concerns about its legitimacy and purpose.

Obfuscation risk due to use of eval
Shell risk from cloning a Git repository during installation

Per-check LLM notes

Network: No network calls were detected, which is not unusual and does not indicate immediate risk.
Shell: The shell execution pattern detected suggests the package might be cloning a Git repository during installation, which could be legitimate but also warrants further investigation to ensure it's not used for malicious purposes.
Obfuscation: The use of eval with restricted environments suggests an attempt to bypass code analysis, indicating potential malicious obfuscation.
Credentials: No clear signs of credential harvesting or secret handling are present.
Metadata: The maintainer has a new or inactive account and lacks a full author name, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

cv_name, cv_def)) func = eval( "lambda " + func_def, { "__buil
in_opt_params} main_opt = eval( main_opt, {"__builtins__": None}, {
pt_params} internal_opt = eval( internal_opt, {"__builtins__": None},
"adabelief") optimizer = eval( optimizer_name, {"__builtins__": None},
x: x try: return eval( activation, {"__builtins__": None},
__): sub_module = __import__(f"{module.__name__}.{name}", fromlist=[""]) register_fennix_modules(sub_module, recurs=recu

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

un git clone result = subprocess.run( ['git', 'clone', '--depth', '1', git_url, str(c

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: sorbonne-universite.fr>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository FeNNol-tools/FeNNol appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 FeNNol

Develop a molecular simulation tool using the FeNNol package that predicts the behavior of molecules under various conditions. This tool will leverage FeNNol's unique capability of integrating force-field methods with neural networks to enhance the accuracy and efficiency of simulations. The application should allow users to input molecular structures and simulate their interactions under different environmental conditions such as temperature, pressure, and solvent type. Key features include:

1. **Molecular Input**: Users can upload or manually input molecular structures in common formats like PDB or SMILES.
2. **Simulation Parameters Setup**: Allow users to specify simulation parameters including temperature, pressure, and solvent details.
3. **Force-Field Selection**: Provide options to choose from a variety of force fields supported by FeNNol.
4. **Neural Network Integration**: Utilize FeNNol's neural network capabilities to refine the simulation outcomes, improving accuracy over traditional force-field methods alone.
5. **Visualization**: Offer real-time visualization of molecular dynamics, allowing users to observe how molecules move and interact under the specified conditions.
6. **Result Analysis**: Provide tools for analyzing the simulation results, such as calculating binding energies, interaction forces, and other relevant properties.
7. **Documentation and Help**: Include comprehensive documentation and interactive help sections to guide users through the process of setting up and interpreting simulations.

To utilize FeNNol, integrate its API calls within your application's backend to handle the heavy lifting of molecular simulations. Ensure that the user interface is intuitive and user-friendly, making it accessible for both beginners and experienced researchers in the field of computational chemistry.