ai-evals

Adversarial Robustness Toolbox

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
6,192 stars1,335 forksPythonUpdated 12/12/2025100% free · open source
What it does

A Python library that lets you test and improve your ML model’s security by generating and defending against adversarial attacks.

When to use it
  • You have a production model and need to measure how vulnerable it is to evasion attacks before launch.
  • Your security team wants to simulate poisoning or extraction attacks on a prototype to assess risk.
  • You’re building a “red‑team” toolset for a data‑driven product and need ready‑made attacks and defenses.
Ready-to-paste prompt
python run_fgsm.py  # runs the script above, loading `mnist_cnn.h5`, attacking the test set, and printing the degraded accuracy
Heads up: ART requires the same deep‑learning framework version that your model was trained with (e.g., TensorFlow 2.x). Mismatched versions cause import errors, so verify `pip show tensorflow` matches the version used to build the saved model.
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/adversarial-robustness-toolbox && curl -fsSL https://workflowstacks.com/api/skills/adversarial-robustness-toolbox/claude-skill -o ~/.claude/skills/adversarial-robustness-toolbox/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

Quick Actions
Details
Creator
Trusted-AI
Language
Python
Category
ai-evals
Published
3/15/2018

Are you the creator of this tool? Claim your listing → and earn 85% of every sale.