AI Analysis
Final verdict: SUSPICIOUS
The package shows no direct network or shell risks, but its metadata raises concerns due to recent creation, low activity, and a single maintainer, suggesting potential malicious intent.
- Recent repository creation
- Low activity level
- Single contributor
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external API interactions.
- Shell: No shell execution detected, indicating the package likely does not execute system commands.
- Metadata: The repository and maintainer history indicate potential red flags such as recent creation, low activity, and single contributor, suggesting possible malicious intent.
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
score 10.0
Git history flags: Repository created very recently: 5 day(s) ago (2026-06-01T04:05:24Z)
Repository created very recently: 5 day(s) ago (2026-06-01T04:05:24Z)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
Only one version has ever been released — brand new packageAuthor "SceneAPI" 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 SceneAPI
Your task is to create a Python-based desktop application that allows users to reconstruct scenes from a set of images using the SceneAPI package. This application will be particularly useful for archaeologists, photographers, and anyone interested in 3D modeling from 2D images. Here’s a detailed breakdown of what your application should achieve: 1. **User Interface**: Design a simple yet intuitive GUI using Tkinter or PyQt. The UI should allow users to select multiple images at once and display a preview of each selected image. 2. **Image Processing**: Implement functionality within your application to process these images before feeding them into the SceneAPI for reconstruction. Ensure the application can handle various image formats (JPEG, PNG, etc.). 3. **Scene Reconstruction**: Utilize the SceneAPI package to perform scene reconstruction. Users should be able to choose between different reconstruction algorithms provided by SceneAPI, such as SfM (Structure from Motion) and MVS (Multi-View Stereo). 4. **Output Options**: After reconstruction, the application should provide options for saving the reconstructed 3D model in various formats (e.g., OBJ, PLY). Additionally, include an option to export the model directly to popular 3D modeling software like Blender. 5. **Visualization**: Integrate a 3D viewer within your application where users can view the reconstructed scene in real-time. This viewer should support basic navigation controls (pan, zoom, rotate). 6. **Help and Documentation**: Include a help section within the application that explains how to use each feature, along with examples of successful reconstructions. 7. **Error Handling and Feedback**: Implement robust error handling to manage issues such as incorrect file formats, insufficient image data for reconstruction, etc. Provide clear feedback messages to guide the user on how to proceed. For this project, focus on leveraging the SceneAPI’s core functionalities to make the scene reconstruction process accessible and efficient for non-expert users. Remember to document your code thoroughly and consider writing a README file for others who might want to extend or modify your application.