Rusty-Stack

v0.2.0 suspicious
4.0
Medium Risk

A comprehensive machine learning environment optimized for AMD GPUs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse through subprocess execution, although the exact purpose remains unclear. Additionally, the package's metadata suggests it might be newly created without detailed maintainer information, raising suspicion.

  • Subprocess execution with unclear intent
  • Lack of maintainer details
Per-check LLM notes
  • Network: No network calls detected, indicating low risk.
  • Shell: Subprocess execution is observed but lacks context to determine benign or malicious intent; further investigation required.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is suspicious due to its newness and lack of maintainer details, but no clear malicious indicators.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • CRATE_NAME] result = subprocess.run(cmd) if result.returncode == 0: return
  • ", CRATE_NAME] fallback = subprocess.run(fallback_cmd) if fallback.returncode != 0: raise
  • rue try: result = subprocess.run( [path, "--version"], capture_output
  • list[str]) -> int: return subprocess.run(cmd).returncode def install_main() -> None: sys.exit(_
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository scooter-lacroix/Stan-s-ML-Stack appears legitimate

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Package is very new: uploaded 3 day(s) ago
  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with Rusty-Stack 
Create a mini-application that leverages the Rusty-Stack Python package to optimize machine learning tasks specifically for AMD GPUs. Your task is to develop a utility that can perform real-time image classification using pre-trained models. This application should include the following features:

1. **Image Upload**: Users should be able to upload images through a simple web interface.
2. **Model Selection**: Provide a dropdown menu where users can choose from different pre-trained models supported by Rusty-Stack, such as ResNet50, VGG16, etc.
3. **Real-Time Classification**: Once an image is uploaded and a model is selected, the application should use Rusty-Stack to classify the image in real-time, taking advantage of AMD GPU acceleration.
4. **Results Display**: After processing, display the top classifications along with their confidence scores.
5. **Performance Metrics**: Include a feature that measures and displays the time taken for image classification, highlighting the performance benefits of using AMD GPUs and Rusty-Stack.

To achieve these goals, you will need to utilize Rusty-Stack's core functionalities, which are designed to optimize machine learning workloads for AMD GPUs. This includes setting up the environment to recognize and utilize AMD GPUs efficiently, loading pre-trained models, and performing inference operations. Additionally, ensure your application is user-friendly and efficient, providing clear instructions and feedback throughout the process.