AI Analysis
Final verdict: SUSPICIOUS
The package exhibits low technical risks such as no network calls, shell executions, obfuscations, or credential risks. However, the metadata risk due to the maintainer's new or inactive account and lack of author information raises concerns about potential supply-chain attacks.
- Metadata risk due to new/inactive maintainer account
- Missing author information
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- 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 maintainer has a new or inactive account with limited package history and missing author information, raising some suspicion but not conclusive evidence of malice.
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 sbobek/acfx appears legitimate
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 acfx
Create a mini-application that helps users understand and improve the decision-making process of a machine learning model using the 'acfx' package. The application should allow users to input data similar to what the model was trained on and receive explanations about how changing certain features could lead to different outcomes. Here’s a detailed plan for the project: 1. **Setup**: Begin by setting up a virtual environment and installing necessary packages including 'acfx'. Also, include other dependencies such as pandas for data manipulation and sklearn for machine learning model creation. 2. **Model Training**: Train a simple binary classification model using sklearn on a publicly available dataset. Ensure the model is not too complex but still demonstrates the capabilities of 'acfx'. 3. **User Interface**: Develop a basic web interface using Flask where users can input their own data points. This interface should accept inputs corresponding to the features used in the training dataset. 4. **Explanation Generation**: Integrate 'acfx' into your application to generate actionable counterfactual explanations for each user input. These explanations should highlight which feature changes would most likely alter the model's prediction. 5. **Visualization**: Implement a visualization component that displays the original prediction alongside the counterfactuals. Use matplotlib or seaborn for plotting. 6. **Interactive Features**: Allow users to tweak individual features and see how it affects both the model's prediction and the set of counterfactuals generated by 'acfx'. 7. **Documentation**: Write comprehensive documentation explaining how the application works, how to install and run it locally, and how to interpret the results provided by 'acfx'. 8. **Testing**: Test the application thoroughly to ensure all components work as expected and provide meaningful insights. 9. **Deployment**: Consider deploying the application to a platform like Heroku or AWS so others can use it without needing to set up their own environment. This project aims to demystify the workings of machine learning models through interactive exploration and explanation, making it easier for non-technical stakeholders to engage with AI systems.