AI Analysis
Final verdict: SUSPICIOUS
The package shows high metadata risk indicators but no immediate technical exploitation methods like network calls or shell executions. However, the unusual metadata suggests potential for future malicious activity.
- High metadata risk due to recent repository creation, low activity, and single contributor.
- No immediate signs of exploitation but potential for supply-chain attack.
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 direct system command execution risk.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: High risk due to recent repository creation, low activity, single contributor, and lack of package history.
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
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 10.0
Git history flags: Repository created very recently: 4 day(s) ago (2026-06-01T10:51:21Z)
Repository created very recently: 4 day(s) ago (2026-06-01T10:51:21Z)Repository has zero stars and zero forksVery few commits: 2 totalSingle contributor with only 2 commit(s) — possibly throwaway account
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Amalan Mahendran" 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 NeEDS4BigDataPy
Create a data analytics tool that leverages the NeEDS4BigDataPy package to handle large datasets efficiently using Generalized Linear Models (GLMs). This tool will be designed for data scientists and analysts who need to perform predictive modeling on vast amounts of data without sacrificing accuracy or computational efficiency. The project will include the following steps and features: 1. **Project Setup**: Install necessary Python packages including NeEDS4BigDataPy, pandas, numpy, and matplotlib. Ensure all dependencies are properly installed and configured. 2. **Data Loading**: Implement functionality to load large datasets into your tool. These datasets could be CSV files or any other structured data format. Consider implementing support for incremental loading to handle extremely large datasets. 3. **Subsampling Methods**: Utilize NeEDS4BigDataPy's subsampling methods to reduce the size of the dataset while preserving key statistical properties. This will allow for faster computation times and more efficient use of resources. 4. **Model Training**: Develop a feature within your tool that allows users to train GLM models on their datasets. Users should be able to select different types of GLMs (e.g., logistic regression, Poisson regression) and specify parameters for model training. 5. **Performance Metrics**: After training models, implement a feature to evaluate the performance of these models using various metrics such as accuracy, precision, recall, and F1-score. Additionally, provide visualizations of model performance using matplotlib. 6. **User Interface**: Design a simple yet effective command-line interface (CLI) for interacting with your tool. Consider adding options for advanced users to customize model training and evaluation processes. 7. **Documentation**: Provide comprehensive documentation for your tool, including setup instructions, usage guides, and examples. This will help other data scientists and analysts understand how to use your tool effectively. 8. **Testing & Validation**: Finally, test your tool with real-world datasets to ensure it performs as expected. Validate the results against known benchmarks or manually calculated values to confirm the accuracy of the tool.