aftab

v0.1.61 suspicious
4.0
Medium Risk

A highly configurable implementation of our approach in the Aftab paper, benchmarking different convolutional neural networks and their effects on the final results.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some unusual metadata characteristics, such as a new or inactive maintainer account without a proper author name, raising concerns about its legitimacy.

  • Metadata risk due to a new or inactive maintainer account
  • Lack of proper author name 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 immediate signs of executing system commands.
  • Obfuscation: The observed pattern appears to be related to code formatting or internal method naming rather than deliberate obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, which raises some suspicion but not enough to conclusively identify it as malicious.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • 1): self._network.eval() observation, frame_count = self.__collect_tra
✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository tahashieenavaz/aftab appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 aftab 
Your task is to create a Python-based mini-application that utilizes the 'aftab' package to evaluate and compare the performance of various convolutional neural networks (CNNs) on a given dataset. This application will serve as a tool for researchers and developers interested in understanding the impact of different CNN architectures on classification tasks. Here’s a step-by-step guide to building this application:

1. **Project Setup**: Begin by setting up your Python environment. Ensure you have Python 3.x installed, along with necessary libraries such as numpy, pandas, matplotlib, and scikit-learn. Additionally, install the 'aftab' package.
2. **Dataset Selection**: Choose a publicly available image dataset suitable for classification tasks, such as CIFAR-10 or MNIST. Download and preprocess the dataset to ensure it is ready for training and testing CNN models.
3. **Model Configuration**: Use the 'aftab' package to define and configure different CNN architectures. The package allows for high configurability, so experiment with varying numbers of layers, filter sizes, and other hyperparameters.
4. **Training and Evaluation**: Implement a function to train each configured CNN model using the dataset. After training, evaluate the models based on accuracy, loss, and possibly other metrics like precision and recall. Use the 'aftab' package's benchmarking capabilities to automate these processes.
5. **Visualization**: Develop a simple visualization module to display the performance of each model. This could include bar charts showing accuracy across different models, or heatmaps illustrating the confusion matrices.
6. **User Interface**: Create a basic command-line interface (CLI) for interacting with your application. Users should be able to select datasets, configure CNN parameters, run benchmarks, and view results.
7. **Documentation and Testing**: Write comprehensive documentation detailing how to use your application, including setup instructions and examples. Also, implement unit tests to ensure the reliability of your code.

Suggested Features:
- Support for multiple CNN architectures out-of-the-box.
- Interactive configuration options for CNN parameters.
- Automated benchmarking and comparison of model performances.
- Customizable visualization options for better insight into results.
- Easy-to-use CLI for non-programmers.

By following these steps and utilizing the 'aftab' package effectively, your application will provide valuable insights into the strengths and weaknesses of different CNN architectures.