semvec/pypi-packages huggingface dataset:
toml-cli 0.8.2
Command line interface to read and write keys/values to/from toml files
Requires Python: >=3.10

# toml-cli

![Build](https://github.com/mrijken/toml-cli/workflows/CI/badge.svg)

Command line interface for toml files.

This can be usefull for getting or setting parts of a toml file without an editor.
Which can be convinient when values have to be read by a script for example in
continuous development steps.

## Install

Install via

`pip install toml-cli`

or

`uv tool install toml-cli`

## Get a value

`toml get --toml-path pyproject.toml tool.poetry.name`

`toml get --toml-path pyproject.toml tool.poetry.authors[0]`

`toml get --toml-path pyproject.toml tool.poetry.name --default marc`

## Search with [JMESPath](https://jmespath.org/)

`toml search --toml-path pyproject.toml tool.uv.index[*].name`

## Set a value

`toml set --toml-path pyproject.toml tool.poetry.version 0.2.0`

`toml set --toml-path pyproject.toml tool.poetry.authors[0] "Marc Rijken <marc@rijken.org>"`

When the index exists, the item is changed.  Otherwise, the item will be added to the list.

## Add a section

`toml add_section --toml-path pyproject.toml tool.poetry.new_section`

## Unset a value

`toml unset --toml-path pyproject.toml tool.poetry.version`
exploitai 0.4.1
AI Model Security Testing Framework for research and education
Requires Python: >=3.11
Keywords: machine-learning, security, privacy, federated-learning, attacks

# ExploitAI

AI Model Security Testing Framework for research and education.

## Overview

ExploitAI is a Python library designed to demonstrate and test security vulnerabilities in machine learning models. It focuses on:

- **Gradient Inversion Attacks** (DLG, iDLG, GIAS)
- **Membership Inference Attacks** (Shokri et al., variations)  
- **Model Poisoning Attacks** (data poisoning, Byzantine attacks)

## Installation

```bash
pip install exploitai
```

## Quick Start

```python
import exploitai as ea

# Load a pre-trained model
model = ea.models.load_model("resnet18", pretrained=True)

# Run gradient inversion attack
attack = ea.GradientInversion(method="idlg")
results = attack.run(model, target_gradients)

# Generate security report
ea.generate_report(results, output="security_audit.html")
```

## Purpose

This library is developed for:

- **Security Research**: Understanding ML model vulnerabilities
- **Educational Use**: Teaching AI security concepts  
- **Defensive Testing**: Evaluating model robustness

**Note**: This tool is intended for defensive security research and education only.

## Development Status

🚧 **Alpha Release** - Core framework and basic attacks in development

## License

MIT License - see LICENSE file for details.