New Short Course: Red Teaming LLM Application By DeepLearning AI

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Welcome to the blog…

Table Of Contents

• Introduction
• What is Red Teaming?
• Who can join this course?
• New Short Course: Red Teaming LLM Application By DeepLearning AI
• What We Learn In This Course?
• What we get after completing this course?
• Summary
• References

Introduction

In this post we discuss about New Short Course: Red Teaming LLM Application By DeepLearning AI, which is recently launched.

• Learn to identify and evaluate vulnerabilities in large language model (LLM) applications.
• Apply red teaming techniques from cybersecurity to ensure the safety and reliability of your LLM application.
• Use an open source library from Giskard to help automate LLM red-teaming methods.

What is Red Teaming?

Red teaming is the practice of rigorously challenging plans, policies, systems and assumptions by adopting an adversarial approach.

A red team may be a contracted external party or an internal group that uses strategies to encourage an outsider perspective.

Who can join this course?

Red Teaming LLM Applications is a beginner-friendly course. Basic Python knowledge is recommended to get the most out of this course.

New Short Course: Red Teaming LLM Application By DeepLearning AI

In this course we learn how to test and find vulnerabilities in our LLM applications to make them safer.

We will learn how to attack various chatbot applications using prompt injections to see how the system reacts and understand security failures.

LLM failures can lead to legal liability, reputational damage, and costly service disruptions.

This course helps us mitigate these risks proactively. Learn industry-proven red teaming techniques to proactively test, attack, and improve the robustness of our LLM applications.

What We Learn In This Course?

In this course:

• Explore the nuances of LLM performance evaluation, and understand the differences between benchmarking foundation models and testing LLM applications.
• Get an overview of fundamental LLM application vulnerabilities and how they affect real-world deployments.
• Gain hands-on experience with both manual and automated LLM red-teaming methods.
• See a full demonstration of red-teaming assessment, and apply the concepts and techniques covered throughout the course.

What we get after completing this course?

After completing this course, we will have a fundamental understanding of how to experiment with LLM vulnerability identification and evaluation on our own applications.

Summary

This approach combines proactive security measures with advanced testing techniques to fortify LLM applications against potential threats, ultimately enhancing their safety and reliability in real-world scenarios.

Also, you can refer this for more learning about LLM Models,

• https://galaxyofai.com/tag/llms/

References

• https://www.deeplearning.ai/short-courses/