🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its network activities and lack of associated metadata like a GitHub repository, which raises concerns about its legitimacy and potential for supply-chain attacks.

network risk due to HTTP requests
lack of associated GitHub repository

Per-check LLM notes

Network: The package makes HTTP requests to external URLs, which could be part of its intended functionality but may also indicate data exfiltration or C2 communication.
Shell: No shell execution patterns were detected in the provided code snippets.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package appears to be new and lacks an associated GitHub repository, which may indicate low activity or a lack of community support.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

try: response = requests.post(submit_url, data=payload, headers={'Content-Type': 'applicat
try: status = requests.get(status_url).text.strip() if status == "FINISHED"
l_num" try: res = requests.get(result_url) return res.text if res.status_code == 20

✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: example.com

✓ 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 "Alper" 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 AlperMSA

Create a bioinformatics tool using Python that allows researchers to perform multiple sequence alignment (MSA) on DNA or protein sequences. Your task is to develop a simple yet powerful web application that integrates the 'AlperMSA' package, which is based on the Clustal Omega algorithm. This application should enable users to upload their sequence data, choose between DNA or protein alignment, and receive an aligned output. Additionally, the app should offer basic visualization options for the aligned sequences and provide downloadable results in various formats such as FASTA or CLUSTAL.

Step 1: Set up your Python environment with Flask or Django to create the web application framework.
Step 2: Integrate the 'AlperMSA' package into your application for performing the MSA.
Step 3: Develop a user-friendly interface where users can input or upload their sequence files.
Step 4: Implement functionality that allows users to specify whether they are aligning DNA or protein sequences.
Step 5: Create a feature that visualizes the aligned sequences in a readable format.
Step 6: Add an option for users to download the aligned sequences in different file formats.
Step 7: Test the application thoroughly to ensure it handles various edge cases and errors gracefully.

Suggested Features:
- User authentication and session management
- Support for multiple file uploads at once
- Advanced options for tweaking alignment parameters
- Integration with a database to save user sessions and results
- Real-time progress updates during long alignments

Utilization of 'AlperMSA':
- Use 'AlperMSA' to handle the core functionality of performing the MSA based on the user inputs.
- Ensure the application correctly interprets user choices regarding sequence type and outputs the aligned sequences accurately.