AI Analysis
Final verdict: SAFE
The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential risks. The main concern is the lack of repository visibility and the maintainer's limited package history, suggesting potential newness rather than malicious intent.
- No network calls detected
- Repository not found, single package by maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communication.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The repository is not found and the maintainer has only one package, which could indicate a low activity level or a new account.
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: utoronto.ca>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Mykhaylo Slobodyanyuk, Jonathan Barenboim" 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 activepathways
Create a web-based mini-application using Flask (a Python micro web framework) that allows users to upload their multivariate omics datasets and perform pathway enrichment analysis using the 'activepathways' Python package. This application will serve as a user-friendly interface for researchers to gain insights into biological pathways relevant to their data. Here are the steps and features you should include: 1. **User Interface Design**: Design a clean and intuitive front-end using HTML, CSS, and JavaScript. Include sections for file upload, parameter selection, and result visualization. 2. **Backend Development**: Implement the backend using Flask. Ensure it handles file uploads securely and efficiently. 3. **Data Processing**: Integrate 'activepathways' to process uploaded datasets. Allow users to select specific parameters for the enrichment analysis, such as p-value thresholds or pathway databases. 4. **Result Visualization**: Provide visual representations of the enrichment results, such as bar charts showing enriched pathways or network diagrams highlighting key pathways and their interactions. 5. **Documentation**: Write comprehensive documentation explaining how to use the application, including setup instructions and examples of input data formats. 6. **Testing and Validation**: Test the application thoroughly with various datasets to ensure accuracy and reliability of the pathway enrichment analysis. Validate the results against known biological pathways. The application should utilize the 'activepathways' package's core functionalities to perform integrative pathway enrichment analysis on the uploaded datasets, providing valuable insights into the biological significance of the data.