• views: 201
• date: 2 May 2025
• posted by: 0nelove

What you'll learn
Understand the core concepts of Large Language Models (LLMs) with real-world examples and security implications.
Break down the architecture of an LLM system, including layers like Application, Integration, Model, and Infrastructure.
Identify key attack surfaces in LLM systems and explore vulnerabilities introduced by APIs, public exposure, and misconfigurations.
Map real-world incidents (like OpenAI vs. DeepSeek) to MITRE ATT&CK/ATLAS frameworks and extract security lessons.
Perform hands-on security testing on LLM APIs using tools like curl browser and simulate common LLM attacks.
Learn practical lab skills through PortSwigger exercises focused on LLM security.
Requirements
No prior cybersecurity or AI experience required.
This course is beginner-friendly and includes step-by-step explanations, real examples, and lab walkthroughs.
Familiarity with basic tech concepts (browsers, APIs) is helpful but not mandatory.
Description
Unlock the world of GenAI Cybersecurity with this beginner-friendly yet in-depth course. Whether you're a cybersecurity enthusiast, AI developer, or IT student, this course provides comprehensive theoretical and practical knowledge to secure Large Language Models (LLMs) — a critical component of today's Generative AI ecosystem.We start with a deep theoretical dive into how LLMs are built using the Transformer architecture, and explore the evolution of neural networks from RNNs to Transformers. You'll gain a solid grasp of innovations like: • Positional Encoding • Self-Attention • Multi-Head AttentionNext, we break down the Anatomy of an LLM System, covering: • Application Layer • AI Model Layer • Integration LayerThen we shift to LLM Attack Surfaces, viewed from both: • Consumer-side risks (e.g., prompt injection, data leakage) • Provider-side vulnerabilities (e.g., model theft, insecure endpoints)You'll explore OWASP Top 10 Risks for LLMs and how to map threats using the MITRE ATLAS framework.This course includes Practical attack demos with explanations: • OLLAMA API Misconfiguration and Mitigation Demo (with NGINX reverse proxy) • PortSwigger Lab: Exploiting LLM APIs with Excessive AgencyWe'll also explore real-world case studies to make learning relatable and practical: • OpenAI vs. DeepSeek – Distillation & model theft risks • Microsoft Tay – Output poisoning and lack of moderation • Wiz's exposed logs – Prompt and data leakage • Chevrolet AI Chatbot – Unexpected real-world agency from chatbots • Ollama API – Exposed endpoints with no authenticationFinally, we conclude with career tips and guidance for aspiring LLM cybersecurity professionals, including: • How to build your foundation in AI/ML • Where cybersecurity meets GenAI • Hands-on practice strategies • Growing your online presence with credibilityTopics • GenAI Cybersecurity • Large Language Models (LLMs) • Generative AI • Transformer architecture • Self-Attention, Multi-Head Attention • Anatomy of an LLM System • LLM Attack Surfaces • OWASP Top 10 Risks for LLMs • MITRE ATLAS framework • Practical Demos • OpenAI vs. DeepSeek • LLM APIs with Excessive Agency
Who this course is for
Cybersecurity beginners and professionals who want to understand AI security
Students and engineers curious about LLMs and how to protect them
Developers integrating LLMs into apps and want to secure their pipelines
Anyone interested in the future of AI safety and hands-on security labs



   
   
   

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