AI Analysis
Final verdict: SUSPICIOUS
The package exhibits low risks in terms of network calls, shell execution, and obfuscation, but its metadata quality is poor, raising concerns about its legitimacy and potential malicious intent.
- Low effort in metadata
- Lack of maintainer history
- Missing author information
Per-check LLM notes
- Network: No network calls detected, which is normal for a package focused on device-specific optimizations.
- Shell: No shell execution patterns detected, aligning with expectations for a package aimed at enhancing torchvision functionality.
- Obfuscation: No obfuscation patterns detected, suggesting low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low effort and could potentially be a new malicious attempt due to lack of maintainer history and missing author information.
Package Quality Overall: Low (1.2/10)
○ 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
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ 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
No suspicious network call patterns found
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: example.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 8.0
4 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with amd-torchvision-device-gfx942
Create a real-time image classification application using the 'amd-torchvision-device-gfx942' package. This application will leverage the advanced capabilities of AMD GPUs to classify images in real-time, providing users with instant feedback on what objects are present within the image. The application should have a simple graphical user interface (GUI) where users can upload an image or capture one from their webcam. Once an image is uploaded or captured, the application should process it through a pre-trained model provided by 'amd-torchvision-device-gfx942', which is optimized for AMD GPUs, and display the top classifications along with confidence scores. Additionally, the application should include features such as saving the classified image with annotations, allowing users to switch between different pre-trained models, and providing an option to view more detailed information about each classification. The goal is to showcase the performance benefits of using 'amd-torchvision-device-gfx942' for real-time image processing tasks.
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