Awesome-AI-Security

Why we included this project

Securing machine learning systems draws on material scattered across a surprising number of places: governance frameworks live in one corner of the web, prompt-injection datasets in another, and the actual scanning tools elsewhere again. This list collects those threads into a single navigable index, with sections covering best practices and controls, testing and evaluation, attack and defense matrices, and datasets, so it works as a starting point rather than a pile of bookmarks. Security engineers and AI platform teams can use it to figure out which standards to adopt and which open tools are worth piloting for a given threat model. The curation is the real value: each section points to the kind of authoritative material teams actually cite, from NIST's AI RMF to OWASP's LLM verification work, which gives readers a fast sense of where the field stands. One caveat: it is an index of external resources, not something you run, so treat it as a planning aid and follow the links to the frameworks or utilities themselves.