AI Analysis
Final verdict: SUSPICIOUS
The package has minimal technical risks but shows signs of low maintainer activity and poor metadata quality, which raises concerns about its legitimacy and potential for misuse.
- Low maintainer activity
- Poor metadata quality
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for functionality.
- Shell: No shell execution patterns detected, indicating no direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
- Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising suspicion but not definitive evidence of malice.
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-gfx1010
Create a mini-application named 'AMD Image Classifier' using Python that leverages the 'amd-torchvision-device-gfx1010' package to classify images on AMD GPUs with specific architecture (gfx1010). This application will serve as a demonstration of how to utilize specialized hardware for image processing tasks, particularly focusing on the performance optimization for AMD GPUs. The application should include the following features: 1. A user-friendly interface that allows users to upload images for classification. 2. Integration of the 'amd-torchvision-device-gfx1010' package to ensure optimal performance on AMD GPUs with gfx1010 architecture. 3. Pre-trained models for common image classification tasks (e.g., CIFAR-10). 4. Real-time feedback on classification results with probabilities for each class. 5. An option to save the classified images along with their predicted labels. 6. Performance metrics to showcase the speed and accuracy improvements when running on AMD GPUs compared to CPU. Your task is to guide the development process from setting up the environment to deploying the final application. Include code snippets where necessary to illustrate key implementation steps.
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