What you'll learn:
- Definition and significance of LLMs in modern AI
- Overview of LLM architecture and components
- Identifying security risks associated with LLMs
- Importance of data security, model security, and infrastructure security
- Comprehensive analysis of the OWASP Top 10 vulnerabilities for LLMs
- Techniques for prompt injection attacks and their implications
- Identifying and exploiting API vulnerabilities in LLMs
- Understanding excessive agency exploitation in LLM systems
- Recognizing and addressing insecure output handling in AI models
- Practical demonstrations of LLM hacking methods
- Interactive exercises including a Random LLM Hacking Game for applied learning
- Real-world case studies on LLM security breaches and remediation
- Input sanitization techniques to prevent attacks
- Implementation of model guardrails and filtering methods
- Adversarial training practices to enhance LLM resilience
- Future security challenges and evolving defense mechanisms for LLMs
- Best practices for maintaining LLM security in production environments
- Strategies for continuous monitoring and assessment of AI model vulnerabilities
LLM Pentesting: Mastering Security Testing for AI Models
Course Description:
Dive into the rapidly evolving field of Large Language Model (LLM) security with this comprehensive course designed for both beginners and seasoned security professionals. LLM Pentesting: Mastering Security Testing for AI Models will equip you with the skills to identify, exploit, and defend against vulnerabilities specific to AI-driven systems.
What You’ll Learn:
Foundations of LLMs: Understand what LLMs are, their unique architecture, and how they process data to make intelligent predictions.
LLM Security Challenges: Explore the core aspects of data, model, and infrastructure security, alongside ethical considerations critical to safe LLM deployment.
Hands-On LLM Hacking Techniques: Delve into practical demonstrations based on the LLM OWASP Top 10, covering prompt injection attacks, API vulnerabilities, excessive agency exploitation, and output handling.
Defensive Strategies: Learn defensive techniques, including input sanitization, implementing model guardrails, filtering, and adversarial training to future-proof AI models.
Course Structure:
This course is designed for self-paced learning with 2+ hours of high-quality video content (and more to come). It’s divided into 4 key sections:
Section 1: Introduction - Course overview and key objectives.
Section 2: All About LLMs - Fundamentals of LLMs, data and model security, and ethical considerations.
Section 3: LLM Hacking - Hands-on hacking tactics and a unique LLM hacking game for applied learning.
Section 4: Defensive Strategies for LLMs - Proven defense techniques to mitigate vulnerabilities and secure AI systems.
Whether you’re looking to build new skills or advance your career in AI security, this course will guide you through mastering the security testing techniques required for modern AI applications.
Enroll today to gain the insights, skills, and confidence needed to become an expert in LLM security testing!
