Token 1.21: Vulnerabilities in LLMs and how to deal with them

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:

• Why should anyone focus on LLM vulnerabilities?

• What kind of vulnerabilities is an LLM susceptible to?

• LLMs are commonly used in conjunction with other services like databases and information retrieval systems. Do these additional services open doors to new attacks?

• How do I protect my models from these attacks?

• Conclusion

• References

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,

• The output of LLM can be manipulated; it can be made to produce biased, offensive, and degenerate content. This can result in customer dissatisfaction and potential lawsuits.

• The other side of the same coin: LLMs, if misused, could generate and spread misinformation at an unprecedented scale, challenging the efforts to maintain integrity in public discourse. Which becomes especially concerning in the year of a few important elections.

• The model can be tricked into revealing personal information from its training corpus.

• Sophisticated phishing attempts using content generated by LLMs could undermine trust in digital communications, making people more skeptical of genuine interactions.

• There's a risk that LLMs could inadvertently generate content that infringes on copyrighted materials, leading to complex legal challenges for creators and users alike.

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:

• Token 1.0: Where are you in the FMOps Infrastructure Stack?

• Token 1.1: From Task-Specific to Task-Centric ML: A Paradigm Shift

• Token 1.2: Use Cases for Foundation Models and Key Benefits 

• Token 1.3: What is Retrieval-Augmented Generation (RAG)?

• Token 1.4: Foundation Models – The Building Blocks

• Token 1.5: From Chain-of-Thoughts to Skeleton-of-Thoughts, and everything in between

• Token 1.6: Transformer and Diffusion-Based Foundation Models

• Token 1.7: What Are Chain-of-Verification, Chain of Density, and Self-Refine?

• Token 1.8: Silicon Valley of AI Chips and Semiconductors

• Token 1.9: Open- vs Closed-Source AI Models: Which is the Better Choice for Your Business?

• Token 1.10: Large vs Small in AI: The Language Model Size Dilemma

• Token 1.13: Where to Get Data for Data-Hungry Foundation Models

• Token 1.14: What is Synthetic Data and How to Work with it?

• Token 1.15: What are Hallucinations: a Critical Challenge or an Opportunity?

• Token 1.16: Understanding RLHF and its use cases

• Token 1.17: Deploying ML Model: Best practices feat. LLMs

• Token 1.18: How to Monitor LLMs?

• Token 1.19: How to optimize LLM Inference

• Token 1.20: Explainable AI techniques and tools for LLMs