Introduction

The popularity of Large Language Models (LLMs) has ever growing since they were introduced. Hugging Face has over 500k models available to fine-tune or use out of the box. arXiv has over 19,000 papers on LLM. Out of those 19,000 papers on LLMs, 2,200 papers were published this year alone i.e. in the past 50 days. There are numerous blog posts detailing how you can use LLMs for your use case. However, vulnerabilities in large language models are talked about very little. Today, we will shift our focus to what could go wrong and discuss vulnerabilities associated with these models.

In this Token, we discuss:

Let’s get started!

Why should anyone focus on LLM vulnerabilities?

The popularity of LLM is increasing with each passing day. In today’s world, when a single model is serving millions of users, a lot of things can go wrong when the LLM’s vulnerabilities are taken advantage of. For instance,

Hence, to make a secure and robust model, it is essential to identify potential vulnerabilities of the specific model.

What kind of vulnerabilities is an LLM susceptible to and how to deal with them?

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Previously in the FM/LLM series: