Llm Pentesting Mastering Security Testing For Ai Models
 

Complete Guide to LLM Security Testing

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

Requirements
Foundational Knowledge of Machine Learning
Awareness of Cybersecurity Principles
Interest in AI and Security
Willingness to Engage in Hands-On Learning
Familiarity with LLMs

Description
LLM Pentesting: Mastering Security Testing for AI ModelsCourse 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!

Overview
Section 1: Introduction

Lecture 1 Introduction

Section 2: All About LLM (Large language model)

Lecture 2 What is LLM and Its Architecture

Lecture 3 LLM Security

Lecture 4 Data Security

Lecture 5 Model Security

Lecture 6 Infrastructure Security

Lecture 7 Ethical Considerations

Section 3: LLM Hacking

Lecture 8 LLM Owasp Top 10

Lecture 9 Exploiting LLM APIs with excessive agency

Lecture 10 Exploiting vulnerabilities in LLM APIs

Lecture 11 Indirect prompt injection

Lecture 12 Exploiting insecure output handling in LLMs

Section 4: Defensive Strategies for LLMs

Lecture 13 Input Sanitization Techniques

Lecture 14 Model Guardrails and Filtering

Aspiring Cybersecurity Professionals,Data Scientists and Machine Learning Engineers,Penetration Testers and Ethical Hackers,IT Security Analysts,Software Developers,Technology Enthusiasts,Students and Researchers