AI Fundamentals and AI Hacking 101
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The AI Fundamentals and AI Hacking 101 ILT teaches students the fundamentals of how AI works under the hood and then how to break it.
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$695.00
Schedule To Be Announced- VILT
The first day of the course focuses on the fundamentals of how AI works. Students will learn and perform labs on topics such as:
- How do neural networks function
- Training of neural networks
- The progression of AI for natural language processing
- Recurrent neural networks (RNN)
- Large Language Models and Attention
- Self-Hosting LLMs and interacting with them programmatically
The hacking portion of the course focuses on penetration testing AI/LLM based applications such as customer facing chatbots by demonstrating how to detect and exploit common AI vulnerabilities such as:
- Prompt Injection
- Sensitive Information Disclosure
- Improper Output Handling
- System Prompt Leakage
- Misinformation
- Excessive Agency
Not only will students learn about these core topics and exploits, but they will also spend hands-on time in a custom-built environment training their own neural networks, tweaking LLMs, exploiting and uncovering vulnerabilities and much more. The online lab features the TCM Vulnerable Chatbot, a customer service chatbot that can interact with customers' tickets and improve its responses via Retrieval Augmented Generation (RAG) using the company's knowledge base.
Who should attend AI Fundamentals and AI Hacking 101 Course
- Penetration testers looking to add AI/LLM pen testing to their tool kit
- Developers working with AI and LLM applications
- Defenders looking to understand AI risks and how they can impact their organizations
- Anyone interested in AI and its risks and dangers
Prerequisites for AI Fundamentals and AI Hacking 101 Course
AI Fundamentals and AI Hacking 101 Course Outline
Resources
FAQs on AI Fundamentals and AI Hacking 101
This course is designed for penetration testers wanting to add AI/LLM testing skills, developers building AI/LLM applications, defenders assessing AI-related organizational risks, and anyone interested in understanding AI risks and vulnerabilities.
A preliminary understanding of penetration testing methodology is suggested, though no formal AI or machine learning background is required.
The course covers neural network fundamentals, natural language processing (NLP), word vectorization, bigrams/trigrams predictive models, recurrent neural networks, and the transformer decoder architecture used in modern LLMs.
Yes, it includes practical labs such as training a neural network for image recognition and a word2vec lab for visualizing word vector representations.
The course aims to build a strong technical foundation in how neural networks and LLMs work, then apply that knowledge to identify and exploit AI-specific security vulnerabilities, including LLM attention mechanisms and related attack surfaces.