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Adversarial Robustness Toolbox

Open-source library for classical ML attacks and defenses

Open source

The Adversarial Robustness Toolbox (ART) is a Python library of evasion, poisoning, model extraction and inference attacks and defenses, written for red and blue teams. It covers classical and deep learning models across many ML frameworks, not LLM prompts. Its first PyPI release was in 2018.

Best for: Teams testing image, tabular or classic ML models against adversarial attacks.

Open source (MIT), an LF AI & Data graduated project created at IBM Research

  • – Maps to OWASP
  • – Maps to MITRE ATLAS
  • – Maps to the EU AI Act
  • – SOC 2 or ISO 27001
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Facts checked November 9, 2026 against the sources listed below. Adversarial Robustness Toolbox does not pay to appear here.

1 Legal nameAdversarial Robustness Toolbox (ART)
2 HeadquartersNot disclosed
3 Founded2018
4 TeamNot disclosed
5 What they testFoundation models, Data leakage, Multimodal
6 FrameworksNot disclosed
7 CertificationsNot disclosed
8 Published pricesFree (MIT)
9 Customers and partnersNot disclosed

Worth knowing before you sign

  • No tagged release since 1.20.1 (Jul 2025), though maintainers still commit.
  • Not built for LLM or agent red teaming.

Go to Adversarial Robustness Toolbox's site

Sources

  1. GitHub: Trusted-AI/adversarial-robustness-toolbox
  2. GitHub: releases

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