AI Analysis
Final verdict: SUSPICIOUS
The package has low risks in terms of network, shell, and obfuscation but exhibits high metadata risk due to signs of abandonment or lack of community involvement. This could indicate potential issues such as unmaintained code or a possible supply-chain attack.
- High metadata risk due to minimal activity
- Suspected abandonment or new creation of the repository
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 risk of command injection or privilege escalation.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The repository shows signs of being abandoned or newly created, with minimal activity and contributors, raising 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 7.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forksVery few commits: 1 totalSingle contributor with only 1 commit(s) — possibly throwaway account
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 MIset
Develop a data analysis tool named 'FeatureSelector' using Python and the 'MIset' library. This tool aims to help data scientists and analysts quickly identify the most relevant features in their datasets using mutual information-based feature selection techniques. The application should allow users to upload a CSV file containing their dataset, select target variables, and apply various feature selection methods provided by the MIset package. The output should include a ranked list of features based on their relevance to the target variable(s), along with visualizations such as bar charts showing the importance scores of each feature. Additionally, the tool should provide options to filter out features below a certain threshold of importance and export the selected features to a new CSV file. Ensure the application has a user-friendly interface, supports multiple target variables, and includes documentation explaining how to use MIset for feature selection in different types of datasets.