AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network activity, shell execution, and code obfuscation. However, the incomplete maintainer's profile and potential inactivity elevate the metadata risk, making it necessary to approach with caution.
- Incomplete maintainer profile
- Potential maintainer inactivity
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
- Credentials: No credential harvesting patterns detected, suggesting the package does not engage in secret or credential theft.
- Metadata: The maintainer has an incomplete profile and appears to be new or inactive, raising some suspicion but not conclusive evidence of malice.
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: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository ICCB-Cologne/CNSistent appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 CNSistent
Create a web-based application using Python and CNSistent that allows researchers to analyze and visualize copy number segments data. This application should allow users to upload their own datasets in common formats such as CSV or TSV, perform basic preprocessing steps including normalization and imputation, segment the data into distinct regions based on copy number variations, and finally provide interactive visualizations of these segments. Additionally, include features like exporting results in various formats (PDF, PNG, etc.), saving sessions for future use, and providing summary statistics about the segmented data. Use CNSistent's tools for imputation, segmentation, and plotting to ensure accurate and efficient processing of the data. Ensure the application is user-friendly with clear instructions and intuitive UI design.