AI Analysis
Final verdict: SAFE
The package exhibits minimal risk indicators with no signs of obfuscation, shell execution, or credential mishandling. However, the metadata risk score is slightly elevated due to potential maintainer inactivity.
- Low network, shell, obfuscation, and credential risks
- Moderate metadata risk due to possible maintainer inactivity
Per-check LLM notes
- Network: The presence of network calls to retrieve files is not uncommon for packages that require additional resources or updates, but should be reviewed for legitimacy and security.
- Shell: No shell execution patterns were detected, which is expected and indicates a lower risk for direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
- Metadata: The package has no suspicious links or email domains, but the maintainer seems new or inactive, raising some concern.
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-mediatek
Create a smart home security system using the MediaTek NeuroPilot SDK for AI Edge LiteRT (ai-edge-litert-sdk-mediatek). This system will use computer vision to monitor a home environment and alert users of potential security threats such as unauthorized entry or suspicious behavior. The system should have the following features: 1. Real-time video monitoring from a connected camera. 2. Object detection capabilities to identify people, animals, and vehicles. 3. Motion detection to trigger alerts when movement is detected. 4. A web interface for viewing live video feeds and receiving alerts. 5. An email/SMS notification system for sending alerts to registered users. 6. Configurable settings for sensitivity and alert types. Here’s how you will utilize the 'ai-edge-litert-sdk-mediatek' package: - Use the SDK to initialize the AI model for object detection and motion analysis on edge devices. - Integrate the SDK into the video processing pipeline to perform real-time analysis of video streams. - Implement the SDK's inference engine to process video frames and detect objects/motion. - Utilize the SDK's optimization features to ensure efficient performance on edge devices. Your task is to design and implement this smart home security system from scratch, providing a detailed explanation of each component and how they interact with the MediaTek NeuroPilot SDK for AI Edge LiteRT.