AI Analysis
Final verdict: SUSPICIOUS
The package shows moderate risk due to its nightly update pattern and the author's limited history of published packages, raising concerns about potential supply-chain risks.
- moderate network risk due to nightly updates
- author has limited package publishing history
Per-check LLM notes
- Network: The detected network call pattern is likely for downloading updates or additional resources, which is common for packages with 'nightly' in their name.
- 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 author has only one published package, which could indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.
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 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-google-tensor-nightly
Create a mini-application named 'EdgeAIImageClassifier' that leverages the 'ai-edge-litert-sdk-google-tensor-nightly' package to classify images captured from a webcam using pre-trained models optimized for edge devices. This application will showcase the real-time classification capabilities of the package on a variety of image categories such as animals, vehicles, and everyday objects. The application should have a simple graphical user interface (GUI) built with Tkinter, where users can select which category of images they want to classify and then use their webcam to capture and classify images in real-time. Additionally, the application should save classified images along with their predictions into a local database for later review. Ensure that your application includes error handling for common issues like camera access denial and model loading failures. Use the 'ai-edge-litert-sdk-google-tensor-nightly' package to load and run the pre-trained models efficiently on the edge device. Document each step of the development process and provide clear instructions for setting up the environment and running the application.