ai-edge-litert-sdk-google-tensor

v2.1.5 suspicious
4.0
Medium Risk

Google Tensor ML SDK for AI Edge LiteRT

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to its newness and the limited activity of its maintainer, despite low risks in other categories such as network calls and obfuscation.

  • Metadata risk due to new package and limited maintainer activity
  • No direct evidence of malicious activity or supply-chain attack
Per-check LLM notes
  • Network: The detected network call is likely for downloading resources and does not inherently indicate malicious activity.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags due to its newness and the limited activity of its maintainer, but there's no clear evidence of typosquatting or other malicious intent.

📦 Package Quality Overall: Low (1.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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 score 1.5

Found 1 network call pattern(s)

  • _url}...') try: urllib.request.urlretrieve(tarball_url, archive_name_local) except Ex
✓ 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 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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-sdk-google-tensor 
Create a real-time object detection and classification mobile app using the 'ai-edge-litert-sdk-google-tensor' package. This app will leverage Google Tensor ML capabilities to perform edge computing, ensuring fast and efficient processing directly on the device without relying heavily on cloud services. Here’s a detailed breakdown of the project requirements and steps:

1. **Project Overview**: Your task is to develop a mobile application that can detect and classify objects in real-time from the camera feed. The application should run smoothly on devices equipped with Google Tensor processors.

2. **Features**:
   - Real-time object detection and classification from live camera feed.
   - User-friendly interface to display detected objects and their classifications.
   - Ability to save images with bounding boxes and labels for future reference.
   - Optional feature: Allow users to add custom objects for training the model.

3. **Technologies and Libraries**:
   - Primary SDK: 'ai-edge-litert-sdk-google-tensor'
   - UI Framework: Kivy or similar cross-platform framework
   - Camera access: Python's opencv or similar library

4. **Implementation Steps**:
   - Step 1: Set up your development environment with necessary packages including 'ai-edge-litert-sdk-google-tensor'. Ensure you have the correct version of TensorFlow Lite for Edge TPU installed.
   - Step 2: Develop a basic user interface using Kivy that includes a camera preview window and a display area for object labels and bounding boxes.
   - Step 3: Integrate the 'ai-edge-litert-sdk-google-tensor' into your project. Utilize its capabilities to load a pre-trained model suitable for object detection tasks.
   - Step 4: Implement the camera feed functionality, ensuring smooth integration with the TensorFlow Lite model for real-time inference.
   - Step 5: Add functionality to draw bounding boxes around detected objects and label them with their corresponding classifications.
   - Step 6: Implement a feature to allow users to save snapshots of the current frame with bounding boxes and labels overlayed.
   - Step 7: Optionally, explore adding support for custom object training within the app. This would involve collecting user data, preprocessing it, and retraining the model accordingly.

5. **Testing**: Thoroughly test the app on different devices with varying lighting conditions to ensure robust performance across scenarios.

6. **Documentation**: Provide clear documentation on how to install the app, use its features, and any limitations or considerations for end-users.

By following these guidelines, you'll create a powerful yet accessible tool for anyone interested in exploring the capabilities of edge computing and machine learning on mobile devices.