GenAI applications are all over the place, sparking the imagination of builders, but at the same time attracting attackers to explore the new attack surface and search for new vulnerabilities and for flawed systems. To protect GenAI applications, organizations use a variety of security mechanisms and security processes, including choosing secure models (text or multimodal), pentest their applications to proactively detect and fix vulnerabilities and deploy runtime screening (“firewall”) to identify attack attempts on the application.
In this talk we will present how to build datasets of GenAI attacks, and how to leverage these datasets to build LLM security assessment, prompt security assessment and firewall, in a modular way that facilitates keeping up with the rapidly evolving attack surface.