AI Analysis
Final verdict: SAFE
The package appears to be primarily focused on feature selection algorithms and does not exhibit significant red flags. The network risk score is moderate due to potential external resource fetching, but overall, the package seems legitimate.
- Moderate network risk due to potential external resource fetching
- No evidence of shell execution, obfuscation, or credential harvesting
Per-check LLM notes
- Network: The presence of network calls could be legitimate if the package is designed to fetch external resources like datasets or models. However, it should be reviewed for unexpected or excessive data transmission.
- Shell: No shell execution patterns were detected, which is expected and does not raise any immediate concerns.
- Obfuscation: No obfuscation patterns detected in the package.
- Credentials: No credential harvesting patterns detected in the package.
- Metadata: The maintainer has only one package and there is a non-HTTPS link, but no clear signs of malicious intent or typosquatting.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
t_ok=True) response = requests.get(url, stream=True) with open(os.path.join(dirpath, "w
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
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://www.computeroptics.ru/KO/PDF/KO45-6/450611.pdf
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Mukhin Artem" 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 InformativeFeatureSelection
Develop a Python-based mini-application that leverages the 'InformativeFeatureSelection' package to perform feature selection on a dataset before applying a machine learning model. Your application should have a user-friendly interface where users can upload their dataset in CSV format. After uploading, the application should automatically preprocess the data, apply the INDI method from the InformativeFeatureSelection package to select the most informative features, and then train a basic classification model (such as logistic regression or a decision tree classifier). The app should display the selected features, performance metrics of the trained model, and provide a simple prediction interface for new data points based on the trained model. Additionally, include a feature to visualize the importance of each feature using bar charts or heatmaps. Ensure your application is well-documented and includes comments explaining how the InformativeFeatureSelection package is utilized in each step of the process.