AI Analysis
Final verdict: SAFE
The package shows minimal risks across all categories, with no network calls, shell executions, or credential-related issues. The only notable concern is the metadata risk due to new or less active authors.
- Low risk scores across all technical categories.
- Metadata risk due to potentially new or less active authors.
Per-check LLM notes
- Network: No network calls detected, which is normal for a package focused on image processing without external dependencies.
- Shell: No shell execution patterns detected, consistent with an image processing tool that does not require system-level commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The authors appear to be new or have an inactive account with only one package on PyPI.
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
Repository collinswakholi/ColorCorrectionPackage appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Collins Wakholi, Devin A. Rippner" 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 ColorCorrectionPipeline
Create a desktop application using Python that allows users to correct the color of their digital images easily. This application will utilize the 'ColorCorrectionPipeline' package to perform flat-field correction, gamma correction, white-balance adjustment, and color-correction on images. The app should have a user-friendly interface where users can upload their images, select which corrections they want to apply, and preview the changes before saving the corrected image. Additionally, the app should provide sliders or input fields to adjust parameters such as gamma value, white balance coefficients, and color correction matrices. Users should also be able to save their correction settings as presets for future use. To achieve this, start by setting up the basic structure of the application using a GUI framework like PyQt or Tkinter. Then integrate the 'ColorCorrectionPipeline' package to handle the image processing tasks. Finally, implement functionality to load and save images, apply corrections based on user inputs, and manage presets.