Master GenAI & LLM cybersecurity, OWASP, MITRE ATLAS mappings, and API attack techniques – practical demos included.

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 ofGenAI Cybersecuritywith 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 witha deep theoretical dive into how LLMsare built using the Transformer architecture, and explore theevolution of neural networks from RNNs to Transformers. You'll gain a solid grasp of innovations like:

• Positional Encoding

• Self-Attention

• Multi-Head Attention

Next, we break down the Anatomy of an LLM System, covering:

• Application Layer

• AI Model Layer

• Integration Layer

Then we shift toGenAI Cybersecurity 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 exploreOWASP Top 10 Risks for LLMsand how to map threats using theMITRE ATLAS framework.

This courseincludes Practical attack demos with explanations:

•OLLAMA API MisconfigurationandMitigationDemo (with NGINX reverse proxy)

•PortSwigger Lab: Exploiting LLM APIs with Excessive Agency

We'll also explorereal-world case studiesto 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 authentication

Finally, we conclude with career tips andguidance for aspiring GenAI Cybersecurity & LLM cybersecurity professionals, including:

• How to build your foundation in AI/ML

• Wherecybersecurity meets GenAI

•Hands-on practice strategies

• Growing your online presence with credibility

Topics

•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
More Info

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