cleverhans-lab/cleverhans

Health v2 · maintenance only

Adoption not part of the score

6450 stars · 1395 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

CleverHans is a Python library for benchmarking machine learning systems' vulnerability to adversarial examples, providing reference implementations of attacks and defenses. It supports JAX, PyTorch, and TensorFlow 2, with tutorials demonstrating its features.

Use cases

• benchmark my model's robustness to adversarial examples
• generate adversarial attacks against a neural network
• test defenses against adversarial perturbations
• implement FGSM or PGD attacks in PyTorch
• evaluate ML model security before deployment
• compare attack success rates across frameworks

When to choose

• you need reference implementations of well-known adversarial attacks (FGSM, PGD, etc.)
• you want to benchmark model robustness across JAX, PyTorch, or TF2
• you are researching adversarial ML and want a citable, standard baseline library

When to avoid

• you need production-grade defense mechanisms rather than research reference code
• you require active feature development or frequent updates
• you need adversarial robustness tooling outside the supported frameworks (e.g., scikit-learn models)

Facets

library · maturity maintenance

machine-learning security benchmarking testing machine-learning security deep-learning artificial-intelligence python adversarial-examples adversarial-attacks adversarial-defenses robustness pytorch tensorflow jax model-evaluation

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