AI Analysis
Final verdict: SUSPICIOUS
The package has minimal risks associated with network activity, shell execution, and code obfuscation. However, its metadata suggests low maintenance and effort, raising suspicions about its legitimacy.
- Low metadata maintenance
- Potential low-effort development
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of code obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
- Metadata: The package shows signs of low maintenance and potential low effort, raising concerns about its legitimacy.
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-gfx1034
Create a small image classification tool using the 'amd-torchvision-device-gfx1034' package. This tool should be able to take an input image from the user, classify it into one of several predefined categories, and display the classification result along with the confidence score. The application should utilize the AMD-specific optimizations provided by 'amd-torchvision-device-gfx1034' to ensure efficient execution on AMD GPUs with GFX1034 architecture. Steps: 1. Set up the environment by installing Python, PyTorch, and the 'amd-torchvision-device-gfx1034' package. 2. Load a pre-trained model compatible with 'amd-torchvision-device-gfx1034'. 3. Implement a function to preprocess images according to the model's requirements. 4. Create a simple GUI using a library like PyQt or Tkinter where users can upload an image. 5. After uploading, the application should classify the image and display the results. 6. Include error handling for common issues such as unsupported file types or network errors when loading the model. 7. Optimize the code to leverage the GPU capabilities specified by 'amd-torchvision-device-gfx1034'. Suggested Features: - Display a list of possible categories the model can classify. - Show a progress bar during the classification process. - Allow users to save the classification results to a local file. - Provide a clean and user-friendly interface for easy interaction.
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