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Textattack

Textattack is an open-source Python framework for generating adversarial examples for NLP models, augmenting text datasets and training NLP models. It provides command-line workflows and Python components for attacks and augmentation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Textattack — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Textattack when you need a Python-based workflow to probe NLP model behavior with adversarial examples or create transformed text for data augmentation.

Who it is for

  • NLP researchers developing or studying adversarial attacks.
  • Model developers examining how NLP systems respond to adversarial inputs.
  • Practitioners augmenting text data or training NLP models.

Documented uses

  • Run attack recipes against NLP models and examine outputs.
  • Develop adversarial attacks with reusable framework components.
  • Generate transformed examples to augment NLP datasets.
  • Use supported workflows to train NLP models.
Before you choose

Requirements & limits

The project is distributed as a Python package via pip, and its public GitHub repository identifies an MIT license. The README specifies Python 3.6 or later; a CUDA-compatible GPU is optional.

  • The framework separates attack construction into goal functions, constraints, transformations and search methods, supporting custom research workflows.
  • The README documents both command-line and Python-module invocation, with a Python Augmenter for transformation-and-constraint-based augmentation.
  • Model and dataset files are downloaded to ~/.cache/textattack/ by default; TA_CACHE_DIR can change the cache location.
  • A CUDA-compatible GPU is optional and may improve speed.
  • The repository provides example scripts for training, attacks and CSV augmentation.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Textattack and inspect available commands

Set up the Python package and find help for its command-line workflows.

Before you start

  • Python 3.6 or later
  • pip

Steps

  1. Install the package with pip install textattack.

  2. Run textattack --help to view the available commands.

  3. Run textattack attack --help to inspect attack-specific options.

Expected outcome

Textattack is installed and its command-line help identifies available attack options.

Guide limitations

  • A CUDA-compatible GPU is optional and may improve speed; installation alone does not run an attack.
  • The README notes that model and dataset files are downloaded to a local cache when needed.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
10 October 2026
Original website title
QData/TextAttack · Official homepage title · retained in its source language
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