🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to its use of 'eval' for obfuscation and subprocess execution without clear context. While there are no immediate signs of malicious activity, the combination of these factors raises concerns about potential misuse.

High obfuscation risk due to 'eval' usage
Unclear context for subprocess execution

Per-check LLM notes

Network: No network calls were detected, which is generally low risk.
Shell: Subprocess execution could be legitimate but requires further investigation into the context and commands used to ensure it's not being abused.
Obfuscation: The use of 'eval' with restricted globals is often used for obfuscation and can be risky as it allows arbitrary code execution under certain conditions.
Credentials: No direct signs of credential harvesting were found, but caution should still be exercised.
Metadata: The package has some red flags including missing author details and a lack of repository link, but no concrete evidence of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

} try: unit_obj = eval(unit_str, {"__builtins__": None}, safe_dict) except Exce
e.split() x = eval(columns[10]) y = eval(columns[11])
umns[10]) y = eval(columns[11]) z = eval(columns[12])
umns[11]) z = eval(columns[12]) V.append([x, y, z]) return
"): xs.append(eval(columns[10])) ys.append(eval(columns[11]))
])) ys.append(eval(columns[11])) zs.append(eval(columns[12]))

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

"w") as log_file: subprocess.run( cmd, stdout=log_file,

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: datascience.edu.pl>

✓ 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

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 MultiMM

Develop a web-based application using Flask in Python that allows researchers to model and visualize chromatin structures ranging from nucleosomes to entire chromosomal territories. This application will leverage the 'MultiMM' package, which specializes in advanced chromatin modeling techniques. The goal is to provide a user-friendly interface where users can upload genomic data and receive visual representations of chromatin structures, aiding in the understanding of complex genetic interactions.

Step-by-Step Development Guide:
1. Set up a Flask environment with necessary dependencies including 'MultiMM'.
2. Design a simple yet intuitive UI using HTML/CSS/JavaScript, allowing users to upload their genomic datasets.
3. Implement backend logic to process uploaded files through 'MultiMM', generating models of chromatin structures at various scales.
4. Integrate visualization tools within the application to display the results dynamically on the webpage.
5. Add documentation and tutorials to guide users on how to use the application effectively.

Suggested Features:
- Upload and process multiple datasets simultaneously.
- Adjustable parameters for different levels of chromatin structure detail.
- Export options for both raw data and visualizations.
- Interactive zoom and pan capabilities for detailed analysis.
- User authentication for saving and retrieving previous analyses.