🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious intent or unusual behavior. It lacks network calls, shell executions, and any form of obfuscation.

No network calls detected.
No shell execution patterns found.

Per-check LLM notes

Network: No network calls detected, which is normal for most PyPI packages unless they require external services.
Shell: No shell execution patterns detected, indicating the package does not execute system commands that could pose a risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package appears to be newly created with no suspicious links or email domains, but the single version and lone package by the maintainer could indicate low activity or inexperience.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Ji Gao" 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 acfgm-pytorch

Create a mini-application that demonstrates the power of the 'acfgm-pytorch' package, which implements the Auto-Conditioned Fast Gradient Method (AC-FGM) optimizer for PyTorch. This application will focus on optimizing a simple neural network model using AC-FGM and comparing its performance against standard optimizers like Adam and SGD. Here are the steps and features you need to implement:

1. **Setup Environment**: Ensure your development environment is set up with Python, PyTorch, and acfgm-pytorch installed.
2. **Define Dataset**: Use a publicly available dataset, such as MNIST or CIFAR-10, for training and testing.
3. **Model Definition**: Define a simple Convolutional Neural Network (CNN) model suitable for the chosen dataset.
4. **Optimizer Implementation**: Implement three different optimizers - Adam, SGD, and AC-FGM from the acfgm-pytorch package.
5. **Training Loop**: Write a training loop that trains the CNN model using each of the three optimizers over multiple epochs.
6. **Evaluation**: After training, evaluate the models on a test dataset and compare their performance metrics such as accuracy and loss.
7. **Visualization**: Plot graphs showing the training and validation loss/accuracy for each optimizer.
8. **Discussion**: Include a brief discussion summarizing the observed differences in performance between the optimizers.

This project aims to showcase not only the ease of use of the acfgm-pytorch package but also the potential benefits of using AC-FGM over traditional optimizers in deep learning tasks.