Cloud Security Podcast by Google

EP144 LLMs: A Double-Edged Sword for Cloud Security? Weighing the Benefits and Risks of Large Language Models

Oct 23, 2023

Kathryn Shih, Group Product Manager in Google Cloud Security, discusses the capabilities and risks of Large Language Models (LLMs). Topics covered include understanding LLMs, their association with intelligence, risks of model tuning, data access control, and security considerations. The podcast provides insights into the nuances and challenges of working with LLMs and offers tips for improving outcomes with them.

29:04

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Episode guests

Kathryn Shih

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Episode notes

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Quick takeaways

LLMs provide advanced language capabilities but have limitations in terms of true understanding and intelligence.

To mitigate the risk of unintended information leaks, organizations can use techniques like retrieval-augmented generation to control data inputs and ensure data privacy and security.

Deep dives

Understanding LLMs: What They Are and What They Can Do

LLM stands for Large Language Model, a highly complex statistical model that can generate linguistically reasonable responses based on input. These models can perform tasks like summarization, language understanding, and even creative writing. While LLMs may seem intelligent, they primarily rely on advanced pattern matching and abstractions rather than true general intelligence. One example is stacking objects with different properties, where LLMs use abstractions and basic autocomplete functionality to complete the task. Overall, LLMs provide advanced language capabilities and can excel at specific tasks but still have limitations in terms of true understanding and intelligence.

Introduction

4min

Understanding Large Language Models and Their Capabilities

3min

The Association of Language and Intelligence and Risks of Model Tuning

3min

Models' ability to fetch information and control data access

3min

Security Considerations for Large Language Models (LLMs)

16min

Guest:

Kathryn Shih, Group Product Manager, LLM Lead in Google Cloud Security

Topics:

Could you give our audience the quick version of what is an LLM and what things can they do vs not do? Is this “baby AGI” or is this a glorified “autocomplete”?
Let’s talk about the different ways to tune the models, and when we think about tuning what are the ways that attackers might influence or steal our data?
Can you help our security listener leaders have the right vocabulary and concepts to reason about the risk of their information a) going into an LLM and b) getting regurgitated by one?
How do I keep the output of a model safe, and what questions do I need to ask a vendor to understand if they’re a) talking nonsense or b) actually keeping their output safe?
Are hallucinations inherent to LLMs and can they ever be fixed?
So there are risks to data and new opportunities for attacks and hallucinations. How do we know good opportunities in the area given the risks?