AI Analysis
Final verdict: SAFE
The package shows low risk indicators with no network or shell activity, and the metadata risk is minimal.
- No network calls detected.
- No shell execution patterns detected.
Per-check LLM notes
- Network: No network calls detected, which is normal and expected for a package focused on optimization problems.
- Shell: No shell execution patterns detected, aligning with the expected behavior of a library designed for solving mathematical optimization models.
- Metadata: The maintainer has only one package, which might indicate a new or less active account but no other suspicious activities were detected.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
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: zib.de>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository SCIP-Interfaces/PySCIPOpt appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Zuse Institute Berlin" 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 PySCIPOpt
Create a Python-based application that utilizes the PySCIPOpt package to solve Mixed Integer Linear Programming (MILP) problems. Your application should be able to handle user inputs for defining a MILP problem, including constraints, variables, and objective functions. It should also provide a user-friendly interface where users can input their specific problem details and receive optimized solutions. Additionally, your app should include features such as problem validation, solution visualization, and performance metrics reporting. Use PySCIPOpt's capabilities to define and solve these problems efficiently, and ensure your application showcases the flexibility and power of PySCIPOpt in handling complex optimization tasks.