Over the next few years, we'll see more organizations building various AI-powered tools and systems. While most AI-powered tools can be built using 3rd-party services and APIs, we'll see more companies using their own LLMs and hosting it in their own private network environments. For one thing, having a self-hosted LLM would guarantee greater control over data privacy and security. In addition to this, companies would gain the much needed flexibility when customizing their LLMs to specific business needs and constraints.

At this point, most professionals are not aware of the security threats and potential security vulnerabilities when building AI-powered applications utilizing self-hosted Large Language Models (LLMs). Similarly, security teams and professionals have not yet adjusted to the new wave of security attacks due to the pace of innovation in the AI space.

One of the more practical techniques to secure these AI-powered applications involves building and using a vulnerability scanner that checks for common vulnerabilities such as prompt injection. In this session, we will use Python to build a custom scanner to help teams identify and mitigate security issues specific to their self-hosted LLMs right away. Finally, we'll also take a look at various strategies on how to mitigate the vulnerabilities found by our scanner.