amd-torchvision-device-gfx1151

v0.0.1.dev0 suspicious
4.0
Medium Risk

Placeholder for amd-torchvision-device-gfx1151

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network usage, shell execution, and obfuscation, but its metadata raises some concerns due to low-effort and potentially suspicious characteristics.

  • Metadata risk of 6/10
  • Low-effort and potentially suspicious signs in metadata
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 direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows several low-effort and potentially suspicious signs, but lacks clear indicators of malicious intent.

📦 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 package
  • Author name is missing or very short
  • Author "" 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-gfx1151 
Create a small-scale image recognition application that leverages the capabilities of the 'amd-torchvision-device-gfx1151' package for optimized performance on AMD GPUs. This application will serve as a tool for users to upload images and receive real-time feedback on object detection within those images. The app should include the following functionalities:

1. User Interface: Develop a simple web interface using Flask where users can upload images.
2. Image Processing: Utilize the 'amd-torchvision-device-gfx1151' package to process images for object detection. Ensure that the package is installed and properly configured to work with your AMD GPU.
3. Object Detection: Implement a model trained on the COCO dataset to detect common objects within the uploaded images. The model should leverage the specific optimizations provided by 'amd-torchvision-device-gfx1151' for improved performance.
4. Results Display: Once processed, display the detected objects overlaid on the original image with bounding boxes and labels.
5. Performance Metrics: Include a feature that measures and displays the time taken for image processing and object detection to showcase the efficiency gains from using 'amd-torchvision-device-gfx1151'.
6. Documentation: Provide comprehensive documentation on how to install and configure the application, including setup instructions for the 'amd-torchvision-device-gfx1151' package.

The goal is to demonstrate the practical application of 'amd-torchvision-device-gfx1151' in enhancing the performance of deep learning models specifically on AMD GPUs.

💬 Discussion Feed

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