aurane

v2.0.0 suspicious
4.0
Medium Risk

A modern domain-specific language for machine learning that compiles to PyTorch

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation and has low activity with a new maintainer, which raises suspicion. However, there are no direct indicators of malicious intent or network/shell risks.

  • High obfuscation risk
  • Low activity and new maintainer
Per-check LLM notes
  • Network: No network calls detected, indicating low risk.
  • Shell: Shell execution is used for local command execution and help documentation, suggesting benign behavior.
  • Obfuscation: The observed patterns suggest an attempt to obfuscate code execution, which may indicate an effort to hide malicious activities or make analysis harder.
  • Credentials: No clear patterns indicative of credential harvesting were found.
  • Metadata: Low activity and new maintainer suggest potential risk, but no clear malicious indicators.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

  • 10 test file(s) detected (e.g. test_ast.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (6867 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 84 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 11 commits in desenyon/aurane
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • f" model.eval()", f" correct = 0",
  • This should not raise compile(code, "<string>", "exec") def test_generated_code_has_proper_indentation(self)
  • thon try: compile(code, "<string>", "exec") is_valid = True except SyntaxError:
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • Python file result = subprocess.run([sys.executable, str(temp_path)], cwd=input_file.parent)
  • nd works.""" result = subprocess.run( ["python", "-m", "aurane.cli", "--help"], captu
  • and help.""" result = subprocess.run( ["python", "-m", "aurane.cli", "compile", "--he
  • 0) """) result = subprocess.run( ["python", "-m", "aurane.cli", "compile", i
  • ing file.""" result = subprocess.run( ["python", "-m", "aurane.cli", "compile", "none
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Desenyon" 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 aurane 
Create a fully functional mini-application using the 'aurane' package, which is a modern domain-specific language for machine learning that compiles to PyTorch. Your goal is to develop a simple image classification tool that can recognize basic categories of objects like cats, dogs, and birds from input images. This application will serve as a practical example of how 'aurane' simplifies the process of building machine learning models.

Step-by-Step Instructions:
1. Begin by setting up your development environment with the necessary libraries, including 'aurane'.
2. Define a dataset consisting of images categorized into 'cats', 'dogs', and 'birds'. You may use publicly available datasets for simplicity.
3. Use 'aurane' to define the architecture of your neural network. Focus on utilizing 'aurane's syntax to make the model definition concise and readable.
4. Implement data preprocessing steps within 'aurane', such as resizing images and normalizing pixel values.
5. Train your model using the defined dataset and evaluate its performance.
6. Integrate a user interface where users can upload an image, and the model predicts which category (cat, dog, or bird) the image belongs to.
7. Finally, optimize your model and discuss any challenges faced during the implementation and how they were overcome.

Suggested Features:
- Incorporate real-time feedback on training progress and accuracy metrics.
- Allow users to see the confidence score of each prediction.
- Provide a feature to save and load trained models.
- Implement a clean and intuitive UI for uploading images and displaying predictions.

How 'aurane' is Utilized:
- 'aurane' allows you to define complex neural network architectures in a more straightforward manner compared to traditional PyTorch. Use this feature to demonstrate the ease of building and training deep learning models.
- Leverage 'aurane's ability to compile directly to PyTorch, showcasing the performance benefits without sacrificing the convenience of high-level programming constructs.

💬 Discussion Feed

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