Name: NDC AI 2025
Start: 2025-11-11
End: 2025-11-13
Location: NDC AI

Talk (60 min)

Doors of (AI)pportunity: The Front and Backdoors of LLMs  

Introduction to AI Security

Security

10 tips to level up your ai-assisted coding

In this demo intensive session Alan will take a deep dive into the architecture of GPT algorithms and the inner workings of ChatGPT. The journey will begin by looking at the fundamental concepts of natural language processing, such as word embedding, vectorization and tokenization. He will then demonstrate how you can apply these techniques to train a GPT2 model that can generate song lyrics, showing the internals of how word sequences are predicted.

Alan will then shift the focus to larger language models, such as ChatGPT and GPT4, demonstrating their power, capabilities, and limitations. The use of hyperparameters such as temperature and frequency penalty will be explained and their effect on the generated output demonstrated. He will then cover the concepts of prompt engineering and demonstrate how Retrieval Augmented Generation (RAG) patterns can be leveraged to create a ChatGPT experience based on your own textual data.

Join me for this session if you want to learn how to harness the power of GPT algorithms in your own solutions.

Inside GPT – Large Language Models Demystified

Machine Learning

GenAI

As models advance seemingly overnight, our ability to interpret their decisions struggles to keep pace. The field of Interpretability isn’t just about explaining outputs or offering post-hoc reasoning— it’s about uncovering how these models actually process and represent information.

This talk explores the latest research shaping AI Interpretability, from Anthropic’s 2024 work on mechanistic interpretability and monosemanticity scaling, to methodologies that have historically aimed at explaining models but fall short for today’s LLMs. We’ll clarify the distinction between explainability and interpretability, their respective use cases, and the current toolkit for understanding black-box models. Along the way, we’ll debunk common myths (like the tendency to anthropomorphize LLMs), and discuss why interpretable AI is essential for both reliability and trust.

For engineers at all levels– whether integrating LLMs into applications or advancing AI research– interpretability is no longer optional. It’s necessary for debugging unexpected model behavior, improving efficiency, and ensuring AI systems remain adaptable and aligned with human goals. If we don’t keep up, we risk being outpaced by models we barely understand, yet still utilize. Attendees will leave with a clearer framework for assessing LLM behavior and practical strategies for applying interpretability techniques to real-world AI systems.

Between the Layers– Interpreting Large Language Models

Ethics

React AI

As we move into an age where “black box” models, particularly LLMs, are becoming more and more commonly used, it becomes even more essential, and at the same time, difficult and complicated, to be able to assess model performance accurately. In this talk, we’ll explore ways in which our models can lie to us, and how we might be able to peer through this confusion to get at the truth.

Can you trust your (large language) model?

Talk (90 min)

NDC AI Afterparty

The presentation covers why AI systems can't tell the difference between instructions and data, making them fundamentally different from traditional software. We'll show actual attack demonstrations including hidden prompts in documents, AI agent goal manipulation, and privacy violations that expose sensitive data.

You'll leave with practical methods for testing your own AI systems, understanding which attacks pose the biggest risks, and building defenses that actually work. All attendees will also get access to our AI red teaming practice platform with vulnerable AI applications, so you can continue developing your AI hacking skills after the talk.

NDC { AI }

Agenda

10:15 - 11:15 (UTC+01)

Can you trust your (large language) model?

Jodie Burchell

Introduction to AI Security

Jim Manico

11:30 - 12:30 (UTC+01)

Inside GPT – Large Language Models Demystified

Alan Smith

Doors of (AI)pportunity: The Front and Backdoors of LLMs

Kenneth Yeung

Kasimir Schulz

13:30 - 14:30 (UTC+01)

How to Break AI Systems (Before Someone Else Does)

Gary Lopez

10 tips to level up your ai-assisted coding

Aleksander Stensby

14:45 - 15:45 (UTC+01)

Between the Layers– Interpreting Large Language Models

Michelle Frost

React AI

Jim Manico

17:00 - 18:30 (UTC+01)

NDC AI Afterparty