• views: 69
• date: 22 January 2026
• posted by: voska89

Hacking & Securing LLMs: Attack & Defense Workshop
What you'll learn
✓ Cybersecurity Professionals & Pentesters: Traditional security researchers looking to pivot their skills into the rapidly growing field of AI and LLM security.
✓ AI Red Teamers: Aspiring security practitioners who want to master automated tools like PyRIT and Garak for auditing model robustness.
✓ DevSecOps Engineers: Developers responsible for deploying LLMs who need to understand how to build and test security guardrails.
✓ Security Consultants: Professionals who need to provide "AI Risk Assessments" or "Risk Scorecards" (like the ones built in the course) to corporate clients.
Requirements
● Linux Command Line Basics: Familiarity with the terminal (moving files, running scripts, and managing services like systemd) is essential.
● No Prior AI Experience Required: We will teach you how LLMs work from a security perspective—you don't need a PhD in Machine Learning!
● Foundational Security Knowledge: A basic understanding of what a "vulnerability" or "exploit" is will help you grasp the offensive concepts quickly.
Description
Course Overview
Build a comprehensive understanding of AI security by constructing a Vulnerable LLM Cyber Range. Large Language Models are increasingly integrated into various systems, from customer-facing chatbots to critical infrastructure. This hands-on course transitions from theoretical AI safety to practical security testing.
You will develop a functional GenAI Security Lab using Python, Streamlit, and local LLMs such as Ollama, Llama 3, and Phi-3. By assuming the roles of both the Attacker (Red Team) and the Defender (Blue Team), you will learn how to identify vulnerabilities, execute exploits, and implement code-level fixes.
What You Will Build and Test
The course features a modular cyber range with over 15 live labs covering the OWASP Top 10 for LLMs. Key topics include
• Prompt Injection: Learn how to bypass chatbot system instructions and safety constraints.
• Remote Code Execution (RCE): Explore how LLMs can be manipulated into executing shell commands on a host server.
• Indirect Injection: Understand how external data sources, such as resumes or emails, can compromise the AI models processing them.
• RAG Data Poisoning: Study methods to corrupt corporate knowledge bases to influence AI output.
• Model Denial of Service: Identify ways to trap autonomous agents in loops or force unauthorized resource consumption.
• Training Data Poisoning: Examine how hidden triggers can be planted within a model's training set.
Target Audience
• Penetration Testers: Professionals looking to expand their skill set into Generative AI security assessments.
• Developers: Software engineers focused on building secure, production-grade LLM applications.
• Security Enthusiasts: Individuals interested in running advanced AI hacking labs on local hardware, including resource-efficient setups like an Intel NUC.
Requirements
• A basic understanding of the Python programming language.
• No dedicated GPU is required, as the labs are optimized for CPU-based local models.
By the conclusion of this course, you will have developed the technical skills, payloads, and practical experience necessary to exploit and patch AI vulnerabilities in professional environments.
Who this course is for
■ If you are already familiar with the OSCP or CEH but feel left behind by the AI boom, this course is for you. We translate classic offensive concepts like "Injection" and "Exfiltration" into the context of LLMs.
■ Building an AI app is easy; securing it is incredibly hard. If you are responsible for deploying Llama 3 or Mistral in a corporate environment, you need to know exactly how an attacker will try to break your guardrails.
■ As companies integrate AI, they are demanding "AI Risk Assessments." This course gives you the tools (like the Streamlit Risk Scorecard) to provide tangible, professional reports to stakeholders.
■ If you are fascinated by how models think—and how they can be tricked—this course provides the lab environment to experiment safely. You'll move past "copy-pasting prompts" and start automating adversarial attacks with Python.
Homepage

https://www.udemy.com/course/oscp-for-ai-hacking-llm/

   
   
   

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