What you’ll learn
Identify and exploit common LLM vulnerabilities like prompt injection and jailbreaks.
Design and execute red teaming scenarios to test AI model behavior under attack.
Analyze and bypass system-level protections in LLMs using advanced manipulation tactics.
Build a testing framework to automate the discovery of security flaws in language models.
Welcome to LLM Red Teaming: Hacking and Securing Large Language Models — the ultimate hands-on course for AI practitioners, cybersecurity enthusiasts, and red teamers looking to explore the cutting edge of AI vulnerabilities.
This course takes you deep into the world of LLM security by teaching you how to attack and defend large language models using real-world techniques. You’ll learn the ins and outs of prompt injection, jailbreaks, indirect prompt attacks, and system message manipulation. Whether you’re a red teamer aiming to stress-test AI systems, or a developer building safer LLM applications, this course gives you the tools to think like an adversary and defend like a pro.
We’ll walk through direct and indirect injection scenarios, demonstrate how prompt-based exploits are crafted, and explore advanced tactics like multi-turn manipulation and embedding malicious intent in seemingly harmless user inputs. You’ll also learn how to design your own testing frameworks and use open-source tools to automate vulnerability discovery.
By the end of this course, you’ll have a strong foundation in adversarial testing, an understanding of how LLMs can be exploited, and the ability to build more robust AI systems.
If you’re serious about mastering the offensive and defensive side of AI, this is the course for you.
Who this course is for:
- AI enthusiasts, prompt engineers, ethical hackers, and developers curious about LLM security and red teaming.
- Beginner to intermediate learners who want hands-on experience in testing and breaking large language models.
- Anyone building or deploying LLM-based applications who wants to understand and defend against real-world threats.
