
Adapt red team tactics for generative AI security as ML evolves, emphasizing black box testing, data handling, and four core components: model, data, application, system.
Explore how ml-based systems inherit application-layer security risks from traditional software, including injection attacks, insecure authentication, weak input validation, cross-site scripting, and threats to confidentiality, integrity, and availability.
Explore the security of the system layer behind ML deployments, focusing on misconfigurations, insecure model deployments, and threats like denial of service, password spraying, vulnerability scanners, and exposed admin interfaces.
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.