AI Analysis
Final verdict: SAFE
The package shows minimal risks across all categories with no evidence of malicious intent. The slight increase in network and metadata risks is not indicative of a supply-chain attack.
- network calls suggest legitimate runtime downloads
- author has only one package, potentially new or less active
Per-check LLM notes
- Network: The network call pattern suggests the package might be downloading additional files during runtime, which could be legitimate if it's for updates or dependencies.
- Shell: No shell execution patterns detected, indicating low risk for direct system command injection.
- Obfuscation: No obfuscation patterns detected, suggesting low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author has only one package, which may indicate a new or less active account, but no other suspicious elements were detected.
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)
ball_url}...') try: urllib.request.urlretrieve(tarball_url, archive_name_local) except Exce
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-sdk-qualcomm-nightly
Develop a real-time object detection application using the 'ai-edge-litert-sdk-qualcomm-nightly' package. This application will run on a Qualcomm device and leverage the AI Edge LiteRT framework for efficient on-device inference. The app should include the following features: 1. **Real-Time Video Capture**: The application should capture video from the device's camera in real-time. 2. **Object Detection**: Use the SDK to perform real-time object detection on the captured video stream. Identify common objects such as people, cars, animals, etc. 3. **Annotation and Display**: Annotate detected objects in the video stream with bounding boxes and labels. Update the annotations in real-time as new frames are processed. 4. **User Interface**: Develop a simple user interface that displays the annotated video stream. The UI should also show statistics like FPS (frames per second), confidence scores of detections, and possibly a list of detected objects. 5. **Configuration Settings**: Allow users to configure settings such as model selection, confidence threshold, and annotation color through the UI. 6. **Logging and Analytics**: Implement logging to record the performance metrics and any errors encountered during runtime. Optionally, integrate analytics to track the usage and performance over time. 7. **Edge Computing Capabilities**: Ensure the application is optimized for edge computing, leveraging the SDK's capabilities for low-latency inference and minimal resource consumption. The 'ai-edge-litert-sdk-qualcomm-nightly' package will be used primarily for initializing the AI model, performing inference on input frames, and managing the lifecycle of the AI session. Additionally, explore advanced features of the SDK such as dynamic model loading and multi-threading support to enhance the application's performance and flexibility.