🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to low maintainer activity and poor metadata quality, despite having no direct evidence of malicious intent or obfuscation.

Metadata risk at 6 out of 10
Shell execution detected requiring further investigation

Per-check LLM notes

Network: No network calls detected, which is normal and safe.
Shell: Shell execution detected might be for version control or running scripts locally, but needs further investigation to confirm legitimacy.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows several red flags indicating low maintainer activity and poor metadata quality, which may suggest potential malicious intent.

🔬 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)

try: out = subprocess.check_output( ["git", "rev-parse", "--show-toplevel"],
d parameters.""" subprocess.run([ sys.executable, # Current Python
-> LAMMPS format subprocess.run([ sys.executable, self.all
imulation parameters subprocess.run( [ sys.executable,

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: stevens.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" 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 AutoREACTER

Develop a fully-functional mini-application named 'ReactionSimulator' that leverages the 'AutoREACTER' package to model chemical reactions using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) within the MuPT framework. This application will serve as an educational tool for students and researchers interested in understanding and visualizing reaction dynamics at the molecular level.

Step 1: Initialize the Project
- Set up a virtual environment for Python.
- Install necessary packages including 'AutoREACTER', 'matplotlib', and 'numpy'.

Step 2: Define Core Features
- Allow users to input basic details of the chemical reaction they want to simulate (reactants, products, initial conditions).
- Utilize AutoREACTER's capabilities to set up and run simulations based on user inputs.
- Implement real-time visualization of the simulation process using matplotlib.

Step 3: Extend Functionality
- Integrate a feature to save simulation results and visualizations for later analysis.
- Add a help section detailing common parameters and their effects on the simulation outcomes.
- Include an option to load predefined reaction scenarios for quick testing and learning.

Step 4: User Interface Design
- Develop a simple yet intuitive graphical user interface (GUI) using Tkinter or PyQt.
- Ensure the GUI clearly displays all necessary input fields, buttons, and output areas.
- Implement error handling to guide users through any incorrect inputs or issues encountered during simulation.

How to Utilize 'AutoREACTER':
- Use 'AutoREACTER' to automatically configure and run LAMMPS simulations based on the user-defined reaction conditions.
- Leverage its tools for analyzing simulation outputs and extracting meaningful insights about reaction dynamics.
- Explore its advanced features for fine-tuning simulation parameters and improving accuracy.