ONLEARN COURSE
LLM Red Teaming & Adversarial Robustness
Proactively identify and mitigate vulnerabilities in large language models to build secure, reliable, and ethical AI applications.
AdvancedEnglish2 sections4 hours357 students
What you’ll be able to do
- Identify and classify common adversarial attack vectors against LLMs.
- Implement various prompt injection, data poisoning, and model manipulation techniques.
- Develop and execute effective red teaming exercises for LLM applications.
- Evaluate the adversarial robustness of LLMs using quantitative metrics.
- Apply defensive strategies and best practices to mitigate LLM vulnerabilities.
- Understand the ethical implications and responsible disclosure practices in LLM security.
Inside this course
2 sections · 10 activities · 4 hours total length
01Module 1: Foundations of LLM Red Teaming
- Introduction to LLM Security & Red Teaming15:00
- Taxonomy of Adversarial Attacks on LLMs20:00
- Setting Up Your Red Teaming Environment25:00
- Hands-on: Basic Prompt Injection Techniques30:00
02Module 2: Advanced Adversarial Attacks & Robustness
- Data Poisoning and Backdoor Attacks20:00
- Model Extraction and Evasion Attacks25:00
- Hands-on: Crafting Sophisticated Adversarial Prompts45:00
- Defensive Strategies: Guardrails and Input Sanitization20:00
- Robustness Evaluation and Metrics20:00
- Project: Red Team a Simulated LLM Application60:00
About this course
Master the art of red teaming Large Language Models (LLMs) to identify and mitigate adversarial vulnerabilities. This advanced course equips you with practical techniques to build more robust and secure AI applications.
Who this is for
- AI/ML Developers looking to build more secure LLM applications.
- Security Engineers interested in specializing in AI security and red teaming.
- Data Scientists and Researchers focused on LLM safety and robustness.
- Anyone responsible for deploying or managing LLMs in production environments.
- Ethical hackers and penetration testers expanding into AI systems.
Before you begin
- Intermediate Python programming skills.
- Basic understanding of Large Language Models (LLMs) and their architecture.
- Familiarity with foundational machine learning concepts.
- Access to a computer with an internet connection and a code editor (e.g., VS Code).
LEARNING EXPERIENCES
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How will I learn?
Work through the course sections and activities in your Onlearn course player, at your own pace. Your progress is saved to your account.
Is this a subscription?
No. Each course is purchased separately with one payment.