  As Large Language Models (LLMs) are deployed and integrated into thousands of
applications, the need for scalable evaluation of how models respond to
adversarial attacks grows rapidly. However, LLM security is a moving target:
models produce unpredictable output, are constantly updated, and the potential
adversary is highly diverse: anyone with access to the internet and a decent
command of natural language. Further, what constitutes a security weak in one
context may not be an issue in a different context; one-fits-all guardrails
remain theoretical. In this paper, we argue that it is time to rethink what
constitutes ``LLM security'', and pursue a holistic approach to LLM security
evaluation, where exploration and discovery of issues are central. To this end,
this paper introduces garak (Generative AI Red-teaming and Assessment Kit), a
framework which can be used to discover and identify vulnerabilities in a
target LLM or dialog system. garak probes an LLM in a structured fashion to
discover potential vulnerabilities. The outputs of the framework describe a
target model's weaknesses, contribute to an informed discussion of what
composes vulnerabilities in unique contexts, and can inform alignment and
policy discussions for LLM deployment.
