Mastering AI Security: A Hindi Guide to Large Language Model (LLM) Pentesting

What you will learn:

LLM architecture and functionality
Data security risks in LLMs
Model security vulnerabilities
Infrastructure security for LLMs
Ethical considerations in LLM development
OWASP Top 10 for LLMs
Prompt injection attack techniques
API vulnerability exploitation
Excessive agency exploitation
Insecure output handling
LLM penetration testing methodologies
Input sanitization best practices
Model guardrails and filtering
Adversarial training for LLMs
Continuous monitoring of LLM vulnerabilities
Real-world case studies and examples

Description

Join our comprehensive course on securing Large Language Models (LLMs)!

This Hindi-language course provides a complete guide to LLM security testing, equipping you with the skills to identify, exploit, and defend against vulnerabilities in AI systems. Whether you're a beginner or an experienced security professional, you'll learn practical techniques to protect your AI infrastructure.

What awaits you:

LLM Fundamentals: Understand the architecture and data processing of LLMs, laying a solid foundation for security analysis.
Critical Security Domains: Explore data, model, and infrastructure security concerns, along with the ethical implications of deploying LLMs.
Hands-on LLM Penetration Testing: Master practical LLM hacking techniques, covering prompt injection, API exploits, excessive agency, and insecure output handling. This includes a unique LLM hacking game for applied learning based on the OWASP Top 10 for LLMs.
Robust Defensive Mechanisms: Learn to implement effective defenses such as input sanitization, model guardrails, filtering, and adversarial training to safeguard your AI models.

The course features 2+ hours of high-quality video tutorials, organized into four concise sections, designed for self-paced learning. Gain the knowledge and confidence to navigate the complexities of LLM security and protect your AI investments. Enroll now!

Curriculum

Introduction

This introductory section sets the stage for the course, providing a clear overview of the key objectives and what learners can expect throughout the learning journey. The introductory lecture provides a comprehensive outline of the course content and its practical applications in securing AI systems.

Understanding Large Language Models (LLMs)

This section dives into the core concepts of LLMs. Lectures cover the definition and architecture of LLMs, explaining how they process information and generate responses. It addresses crucial security aspects related to data, model integrity, infrastructure protection, and the ethical responsibilities involved in developing and deploying LLMs. Lectures will cover LLM architecture in detail, data security best practices, common model vulnerabilities and methods for mitigating them, and the importance of securing the underlying infrastructure.

Practical LLM Penetration Testing

This hands-on section equips learners with practical penetration testing skills specific to LLMs. Lectures cover the OWASP Top 10 vulnerabilities for LLMs, demonstrating how to exploit weaknesses such as prompt injection attacks, API vulnerabilities, excessive agency exploitation, and insecure output handling. Learners will engage in practical exercises, including a unique LLM hacking game, solidifying their understanding of these vulnerabilities and how to identify and mitigate them in real-world scenarios.

Defensive Strategies for LLMs

This section focuses on proactive defense strategies to enhance the security of LLMs. The lectures will address effective defense mechanisms, providing learners with the knowledge to implement robust security protocols. The focus is on implementing effective countermeasures such as input sanitization techniques, building secure model guardrails, filtering mechanisms, and employing adversarial training to proactively strengthen the resilience of LLMs against various attacks. This section emphasizes building a secure framework around LLMs, ensuring long-term protection.