amd-torch-device-gfx1103

v0.0.1.dev0 suspicious
4.0
Medium Risk

Placeholder for amd-torch-device-gfx1103

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network, shell, and obfuscation activities, but the metadata risk score is elevated due to missing maintainer history and author information.

  • metadata risk of 6/10 due to missing maintainer history and author information
  • placeholder description suggests a lack of development effort
Per-check LLM notes
  • Network: No network calls detected, which is normal for a device-specific library.
  • Shell: No shell executions detected, consistent with the expected behavior of a library focused on device compatibility.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows several red flags including lack of maintainer history and missing author information, suggesting low effort or potential 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-torch-device-gfx1103 
Create a real-time image processing application using PyTorch and the 'amd-torch-device-gfx1103' package. This application will leverage the AMD GPU architecture specifically designed for high-performance computing tasks, allowing users to perform advanced image transformations and analyses on their local machine. The app will have several key functionalities:

1. **Image Loading & Display**: Users can load images from their local file system and display them in a user-friendly interface.
2. **Real-Time Transformations**: Implement real-time transformations such as resizing, rotating, and applying filters like grayscale or sepia tone directly on the loaded image.
3. **Advanced Image Processing**: Utilize the power of PyTorch and the 'amd-torch-device-gfx1103' package to apply more complex operations such as edge detection, noise reduction, and color correction.
4. **Save Processed Images**: Allow users to save the processed images back to their local file system.
5. **Performance Monitoring**: Include a feature to monitor the performance of the image processing tasks, showcasing the efficiency gains from utilizing the 'amd-torch-device-gfx1103' package.

The 'amd-torch-device-gfx1103' package is crucial for optimizing the PyTorch operations to run efficiently on AMD GPUs with the GFX1103 architecture. It ensures that all image processing tasks are offloaded to the GPU, thereby significantly reducing processing time and enhancing the overall user experience. Your task is to design and implement this application, ensuring that it is both functional and visually appealing.

💬 Discussion Feed

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