AI Analysis
Final verdict: SUSPICIOUS
The package shows low individual risks in terms of network, shell, and obfuscation activities. However, the metadata risk score is moderately high due to the maintainer's new or inactive account and lack of community engagement, which raises concerns about its legitimacy.
- Metadata risk score of 4 out of 10
- Maintainer has a new or inactive account
Per-check LLM notes
- Network: No network calls suggest the package does not engage in unexpected external communications.
- Shell: No shell executions indicate the package does not run system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting legitimate usage.
- Metadata: The maintainer has a new or inactive account and the repository lacks community engagement, raising some suspicion.
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
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Stemonitis, SciWare LLC" 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 AutoStatLib
Create a fully-functional mini-app called 'DataInsightPro' using the Python package 'AutoStatLib'. This app will serve as a user-friendly interface for conducting basic statistical analyses on datasets. Users should be able to upload their dataset (CSV format), select columns of interest, and then perform various statistical operations such as mean, median, mode, standard deviation, variance, correlation coefficients, and histograms. Additionally, the app should provide visualizations for the data through bar charts, pie charts, and scatter plots to help users understand the relationships between variables. The app should have a clean, intuitive UI built with Streamlit or Dash, allowing users to interactively explore their data. Upon uploading a dataset, users should see summary statistics for all numerical columns, and they should be able to choose specific columns to analyze further. The results of each analysis should be displayed in a structured format, including both numerical summaries and visual representations. Utilize 'AutoStatLib' for its core statistical functions. For instance, use it to calculate descriptive statistics like mean, median, etc., and to generate correlation matrices. Integrate any visualization libraries like Matplotlib or Seaborn to display the data visually. Ensure that the app handles errors gracefully, such as incorrect file formats or missing data, providing clear error messages to the user. As an additional feature, implement a simple machine learning model (e.g., linear regression) using scikit-learn, which predicts one variable based on another, using the data provided by the user. Use 'AutoStatLib' to evaluate the performance of the model by calculating metrics like R-squared and Mean Squared Error. Finally, ensure the app is well-documented, with clear instructions on how to install dependencies and run the application.