Security tools
Adversarial Robustness Toolbox
Test machine learning models against evasion, poisoning, extraction and inference attacks.
6.3K GitHub starsAs of Checked
Where it fits
What you can use it for
Security teams testing models across text, images, audio and tabular data.
Probe models and applications for security weaknesses.
Before you adopt
What to check for your team
Select threat models and attacks that fit the model and data. A benchmark result does not establish application security.
Get help evaluating the fitLicence and scope
MIT
Applies to this repository. Dependencies, model weights, datasets and hosted services can have separate terms.
Read the upstream licenceSupport the work
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Use the project's contribution guidance to report issues, help with documentation or contribute code. Follow any published funding links if you want to support the maintainers financially.
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