🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to its execution of shell commands and novelty, which could indicate potential supply-chain attack vectors.

High shell risk due to execution of shell commands
New package with limited maintenance history

Per-check LLM notes

Network: The package makes network calls which could be legitimate if it's designed to fetch external data or resources.
Shell: Executing shell commands poses a high risk as it can be used for unauthorized actions or data exfiltration.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting secure handling of sensitive information.
Metadata: The package is new and maintained by a single author with limited history, which could indicate potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

UG URL response = requests.get(url) response.raise_for_status() # Raise an exc

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

thon command result = subprocess.run(command, capture_output=True, text=True) return pri

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: tmu.edu.tw

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Tony Eight Lin" 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 MolMiner

Create a chemical analysis tool called 'MoleculeExplorer' using the Python package 'MolMiner'. This tool will allow users to input SMILES strings of molecules and perform various analyses on them. Here’s a detailed breakdown of what the application should do:

1. **User Interface**: Develop a simple, intuitive command-line interface where users can input SMILES strings.
2. **Data Processing**: Utilize MolMiner to convert the inputted SMILES strings into molecular structures and extract key information such as molecular weight, logP values, and functional groups.
3. **Visualization**: Implement basic visualization capabilities to display the molecular structure graphically. Use MolMiner's built-in plotting functionalities if available.
4. **Analysis Features**:
- Calculate and display molecular properties like polarity, solubility, and reactivity based on the extracted data.
- Provide a summary of the molecule's potential biological activities based on its structural features.
5. **Output**: Present the results in a structured format (e.g., JSON) that can be easily exported or further processed.

Suggested Features:
- Add support for batch processing of multiple SMILES strings.
- Integrate a feature to save the analysis results into a local database or file.
- Offer an option to compare two different molecules side by side.

How to Utilize MolMiner:
- Use MolMiner's functions to parse and standardize the SMILES strings.
- Leverage MolMiner's API to calculate molecular properties efficiently.
- Employ MolMiner's visualization tools to generate graphical representations of the molecules.