AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity such as network calls, shell executions, or obfuscation. The primary concern lies with the metadata quality and the new maintainer, but these alone do not indicate a supply-chain attack.
- Low risk scores across all technical indicators
- Concerns about metadata quality and new 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 of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
- Metadata: Low risk due to lack of suspicious elements, but concerns about new maintainer and low metadata quality.
Package Quality Overall: Medium (5.8/10)
✦ High
Test Suite
9.0
Test suite present — 6 test file(s) found
6 test file(s) detected (e.g. test_leakage.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (10800 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
29 type-annotated function signatures detected in source
✦ High
Multiple Contributors
8.0
Active multi-contributor project
3 unique contributor(s) across 100 commits in alvaromc317/asglSmall but multi-author team (3–4 contributors)
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 alvaromc317/asgl appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author "Alvaro Mendez Civieta" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with asgl
Create a Python-based web application that allows users to upload datasets and perform high-dimensional regression analysis using the 'asgl' package. The application should enable users to select between linear, quantile, and logistic regression models and apply various penalties to handle high-dimensional data effectively. Here’s a detailed breakdown of the project requirements: 1. **Setup**: Begin by setting up a virtual environment and installing necessary packages such as Flask for web development, pandas for data manipulation, and 'asgl' for regression analysis. 2. **User Interface**: Design a simple yet intuitive user interface where users can upload their CSV files containing numerical data. Include options for selecting the type of regression model and specifying any desired penalty methods. 3. **Data Processing**: Once the file is uploaded, preprocess the data within your application. This includes handling missing values, encoding categorical variables if necessary, and splitting the dataset into training and testing sets. 4. **Model Training**: Use the 'asgl' package to train the selected regression model on the processed data. Ensure that the application supports both penalized and non-penalized models. 5. **Result Presentation**: Display the results of the regression analysis in a comprehensible format. Include coefficients, R-squared values for linear models, pseudo R-squared for logistic models, and other relevant metrics. Additionally, provide visualizations like scatter plots for linear models or ROC curves for logistic models. 6. **Advanced Features**: Implement advanced features such as cross-validation to optimize hyperparameters and compare different models. Also, allow users to download the trained model or the output results in a downloadable format. 7. **Testing and Validation**: Rigorously test the application with various datasets to ensure accuracy and robustness. Validate the model performance using known benchmarks or datasets. 8. **Documentation**: Provide comprehensive documentation explaining how to use the application, including examples and common troubleshooting tips. The goal of this project is to create a tool that makes high-dimensional regression analysis accessible and understandable for researchers and data analysts who might not have extensive programming backgrounds.
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