AI Analysis
Final verdict: SAFE
The package shows minimal risks with no signs of malicious activities such as shell execution or credential harvesting. The network and metadata risks are slightly elevated but do not strongly suggest a supply-chain attack.
- network calls during runtime
- single package from author
Per-check LLM notes
- Network: The network call pattern suggests the package may be downloading additional resources during runtime, which could be legitimate for updates or additional libraries but should be scrutinized.
- Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author has only one package on PyPI which might indicate a new or less active account, but no other suspicious elements were found.
Package Quality Overall: Low (2.4/10)
○ 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
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
4 type-annotated function signatures (partial)
○ 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)
rom {url}...') try: urllib.request.urlretrieve(url, archive_path) except Exception as e: #
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-intel-nightly
Create a real-time object detection application using the 'ai-edge-litert-sdk-intel-nightly' Python package, which is built on top of Intel's OpenVINO technology for AI Edge LiteRT. This application will run on a local machine or edge device and utilize a pre-trained model to detect objects in video streams from a webcam or video file. The application should have the following features: 1. **Real-Time Video Stream Processing**: Capture live video from a webcam or process a video file in real-time. 2. **Object Detection**: Use a pre-trained model provided by the 'ai-edge-litert-sdk-intel-nightly' package to detect various objects within the video stream. 3. **Visualization**: Display the video stream with bounding boxes around detected objects and labels identifying each object. 4. **Customization**: Allow users to select different pre-trained models available in the package, adjust detection thresholds, and choose between webcam input or video file input. 5. **Performance Metrics**: Optionally, display performance metrics such as FPS (frames per second) and inference time. 6. **User Interface**: Develop a simple command-line interface (CLI) for user interaction, including options to start and stop the detection process, switch between different input sources, and adjust settings. The application should leverage the 'ai-edge-litert-sdk-intel-nightly' package for its ability to optimize AI models for edge devices, ensuring efficient and fast object detection without the need for cloud-based processing. Your task is to outline the steps required to set up this application, including installing the necessary packages, configuring the environment, and writing the code to achieve the specified functionality.