Short Course

Red Teaming LLM Applications

In Collaboration With




1 Hour


Matteo Dora Luca Martial

  • 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 you’ll learn in this course

Learn how to test and find vulnerabilities in your LLM applications to make them safer. In this course, you’ll 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 you mitigate these risks proactively. Learn industry-proven red teaming techniques to proactively test, attack, and improve the robustness of your LLM applications.

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.

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

Who should join?

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


Matteo Dora

Matteo Dora


Lead LLM Safety Researcher at Giskard

Luca Martial

Luca Martial


Product Lead at Giskard

Course access is free for a limited time during the DeepLearning.AI learning platform beta!

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