AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in terms of network, shell, and obfuscation activities. However, the metadata risk score is elevated due to the maintainer having only one package and no associated GitHub repository.
- Low risk in network, shell, and obfuscation activities.
- Elevated metadata risk due to limited maintainer activity.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network functionality.
- Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious intent related to stealing secrets.
- Metadata: The maintainer has only one package and no associated GitHub repository, which raises some suspicion but not conclusive evidence of malice.
Package Quality Overall: Low (2.4/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Brief PyPI description (234 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
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: tensorflow.org
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Google AI Edge Authors" 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 ai-edge-litert-nightly
Your task is to develop a real-time object detection mini-app using the 'ai-edge-litert-nightly' package, specifically designed for deployment on mobile and embedded devices. This application will leverage the lightweight and efficient capabilities of LiteRT to perform object detection tasks directly on-device, without the need for cloud processing. The app will serve as a practical example of how AI can be integrated into edge computing environments for immediate and responsive applications. Step 1: Set up your development environment. Ensure you have Python installed and set up a virtual environment. Install the 'ai-edge-litert-nightly' package and any additional dependencies required for your project. Step 2: Design and implement the user interface. Your app should include a camera feed from the device's camera and display detected objects in real-time. Consider adding features like zooming, panning, and toggling between different models or detection modes if supported by the package. Step 3: Integrate the LiteRT model for object detection. Use the 'ai-edge-litert-nightly' package to load a pre-trained model suitable for real-time object detection. Customize the model if necessary to optimize performance and accuracy for your specific use case. Step 4: Implement the detection logic. Write the code that captures frames from the camera, processes them through the LiteRT model, and draws bounding boxes around detected objects. Ensure that the detection process is as fast as possible to maintain real-time performance. Step 5: Test and refine your application. Test the app on various devices and scenarios to ensure it performs well under different conditions. Make adjustments to the model, UI, or detection parameters as needed to improve performance and usability. Suggested Features: - Support for multiple object classes and customizable detection thresholds. - Option to save detected images or video clips for later analysis. - User-friendly interface for switching between different LiteRT models or configurations. - Real-time performance metrics such as FPS (frames per second) displayed within the app. By completing this project, you'll gain hands-on experience with deploying AI models on edge devices, understand the trade-offs between model size, accuracy, and speed, and learn how to integrate complex machine learning functionalities into practical applications.