AI Analysis
Final verdict: SUSPICIOUS
The package exhibits minimal technical risks but shows signs of low effort and potentially suspicious maintainer behavior, raising concerns about its legitimacy.
- Metadata risk score of 7 out of 10
- Low effort and suspicious maintainer behavior
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low effort and suspicious maintainer behavior, increasing the likelihood of potential 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-gfx906
Create a mini-application that leverages the 'amd-torchvision-device-gfx906' package to optimize image processing tasks on AMD GPUs with GFX906 architecture. This application will serve as a tool for developers and researchers to efficiently preprocess and augment images for deep learning models, particularly those using PyTorch. The app should include the following features: 1. Image loading and display functionality. 2. Preprocessing capabilities such as resizing, normalization, and converting images to tensor format suitable for input into neural networks. 3. Advanced image augmentation techniques like rotation, flipping, and color jittering, which are optimized for AMD GPUs. 4. A user-friendly interface for selecting images from a local directory and applying various preprocessing and augmentation operations. 5. Integration with PyTorch for seamless data pipeline creation. The 'amd-torchvision-device-gfx906' package is utilized to ensure that all image processing tasks are performed with optimal performance on AMD GPUs. Specifically, it should be used to accelerate the execution of torchvision transforms and other image manipulation functions on devices with GFX906 architecture. Your task is to design and implement this application from scratch, ensuring that it showcases the unique benefits of using 'amd-torchvision-device-gfx906' for image processing tasks.
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