This course goes beyond traditional security testing to incorporate novel adversarial machine learning, Responsible AI (RAI) violations, and emerging threats to AI agents. Participants will gain hands-on experience with the latest attack vectors while learning defensive strategies.


The course emphasizes practical application through a custom-built red teaming platform that simulates real-world AI deployments. Participants will work with live AI systems—not mockups or demonstrations—to experience the unpredictable nature of AI vulnerabilities firsthand. Our lab environment includes vulnerable LLM applications, multi-agent systems, and multimodal AI tools that mirror enterprise deployments.

Each module combines theoretical understanding with immediate practical application. When we cover prompt injection techniques, participants immediately test them against live systems. When we discuss automated red teaming, participants build and deploy their own attack workflows using open source tools. This learn-by-doing approach ensures that participants leave with both conceptual knowledge and muscle memory for executing these techniques.

The competitive lab environment includes CTF-style challenges with scoring and leaderboards, making the learning process engaging while building the adversarial mindset essential for effective red teaming.

By the end of this training, participants will be able to:


- Systematically assess LLM applications for prompt injection, data extraction, and jailbreak vulnerabilities
- Execute advanced attacks including Crescendo, Greedy Coordinate Gradient (GCG), Prompt Automatic Iterative Refinement (PAIR), and Tree of Attacks with Pruning (TAP).
- Identify RAI violations across bias, privacy, misinformation, and harmful content categories.
- Leverage automation tools for offensive AI security, benchmarking, and agent building.

- Lab environment: Access to a custom-built platform to AI red teaming.
- Code samples: Complete setup with all tools and target applications .
- Digital Workbook: 400+ slides covered in the course for future reference.


- Laptop with access to the internet.
- Familiarity with the Python programming language and being able to write simple scripts.
- Background in machine learning is not required.


- Module 1: AI Security Landscape
- Module 2: LLM Attack Fundamentals
- Module 3: Automation and Scale
- Module 4: Introduction to Agents and Agentic Systems
- Module 5: Open Source Tooling for AI Red Teaming

- Module 6: Advanced Attack Techniques
- Module 7: Multimodal Models
- Module 8: Building Defenses and Mitigations
- Module 9: Responsible AI Red Teaming

2 Days

AI Red Teaming: Attacks on LLMs, Agents, and Multimodal Systems

Machine Learning

Security Tooling

This hands-on workshop will equip you with practical skills to implement your own MCP servers, clients and MCP-enabled knowledge bases, connecting your organizational knowledge to advanced AI models in a controlled, secure manner.

Introduction to Model Context Protocol and its business applications

Core MCP concepts: Server/client architecture, agents, resources, and tool calling

RAG fundamentals and architecture patterns

Hands-on exercises: Building a local MCP server and RAG pipeline

Implementing solutions in AWS Bedrock Knowledge Bases

Preparing for production: Security considerations and optimization strategies

Final exercise: Exposing your Knowledge Bases through your MCP server

Basic Python or JavaScript programming knowledge

Familiarity with AI/ML concepts (helpful but not required)

Laptop with necessary development environment

This training is targeted at developers and people with a medium+ technical skill level and interest for AI.

Context Is King: Two Days to AI Mastery with MCP & RAG

Using this foundational knowledge, we will move into understanding responsible AI (RAI) practices, covering topics like fairness (bias evaluation and mitigation), explainability, and privacy. Attendees will explore actionable strategies to incorporate RAI principles into their workflows, ensuring models are not only functional but also ethical.

Throughout this workshop, we will use hands-on exercises using Python-based tools and provided datasets, with options to work either on personal laptops or hosted environments like Google Colab.

Machine Learning Foundations for Developers

Many developers, data scientists and companies are rushing to adopt and leverage these developments in their business process, products and solutions.

What do I need to consider when developing an LLM based solution?

What is a generative pre-trained transformer, and how does it work?

How can these models be leveraged by application developers to deliver real business value?

In this workshop, you will explore how to leverage the OpenAI and 3rd party models to develop AI enhanced solutions. You will learn options for hosting and consuming different models and develop code and prompts to explore their capabilities. The integration with company knowledgebases to provide a ChatGPT experience tailored to your business requirements will be covered along with the concepts of using prompt engineering to influence the outputs generated by the model.

Hands-on labs using the latest LLM frameworks will be available in C# and Python, ranging from walk-through exercises to advanced challenges and group activities.

Open AI & GPT Workshop

.NET

The course blends actionable guidance (e.g., AI-assisted coding security) with hands-on insights into securing AI itself (e.g., protecting model endpoints, managing drift, and red teaming AI pipelines). After completing this workshop, participants will be equipped with both the technical know-how and the processes needed to embed security into every stage of their AI-driven products.

Day 1: Using AI to Build Secure Software


Key AI security concepts and common threats

Overview of AI-related vulnerabilities in software development

Quick review of how attackers exploit AI-driven software

Understand the foundational principles of AI security

Recognize the broad threat landscape surrounding AI

Using AI-powered tools (e.g., ChatGPT, Copilot) for code generation

Common pitfalls and vulnerabilities (injected flaws, licensing, dependency issues)

Assess the security implications of AI-driven code generation

Adopt best practices to minimize vulnerabilities introduced by auto-generated code

Integrating security throughout AI/ML system development (planning, data ingestion, modeling, deployment)

Secure DevOps and MLOps practices (CI/CD with security gates)

Handling sensitive data used to train AI models

Incorporate security gates into each phase of AI software development

Properly manage and protect training data and models

Unique threat modeling considerations for AI/ML pipelines (data poisoning, model extraction)

Differences between typical web-app threat modeling vs. AI-centric threats

Identify critical assets and attack vectors in AI workflows

Apply threat modeling to preempt attacks unique to AI pipelines

5. OWASP Top 10 for Large Language Model (LLM) Applications

Top vulnerabilities specific to LLM-based applications

Prompt injection, data exfiltration, malicious chat manipulation

Recommendations, mitigations, and real-world case studies

Recognize and remediate LLM-specific security flaws

Secure LLM-based features in a production environment

Third-party libraries, datasets, and pre-trained models

Checking trustworthiness of external dependencies (data lineage, library patch levels)

Understand how AI supply chain vulnerabilities can undermine entire systems

Implement best practices to vet and protect external model/data sources

Types of adversarial attacks (evasion, poisoning, model inversion)

Techniques to detect and mitigate adversarial inputs

Gradient masking pitfalls and robust model training

Recognize adversarial attacks and how they manifest

Integrate detection and defense strategies into AI models

How to structure AI-specific penetration tests

Tools and frameworks for adversarial testing

Red vs. Blue Team exercises for AI pipelines

Conduct targeted security assessments on AI systems

Operationalize red teaming to continuously improve defenses

Lifecycle management for AI models in production

Security-driven versioning and rollback strategies

Responding quickly to emerging threats or discovered vulnerabilities

Implement robust model update/patching workflows

Reduce downtime and exposure during patch cycles

5. AI Model Interpretability and Security

Why interpretability is critical for detecting malicious behavior or biases

How more interpretable models can reveal vulnerabilities (model extraction, inversion attacks)

Balance interpretability with security concerns

Use interpretability tools to spot anomalies and potential threats

Securing AI workloads and vector databases with strict role-based and zero-trust principles

Credential management for AI model endpoints (API tokens, ephemeral credentials)

Architect AI systems with principle-of-least-privilege at every layer

Apply zero-trust approaches to protect critical AI resources

7. AI Model Drift and Security Monitoring

How models degrade over time (concept drift, data distribution shift)

Continuous monitoring and anomaly detection in production

Mitigating risks introduced by drift (potential new vulnerabilities)

Set up effective monitoring to detect model performance and security anomalies

Know when/how to trigger model retraining or security re-evaluations

NDC { AI }

Workshops

09:00 - 17:00 (UTC+01)

Open AI & GPT Workshop

Alan Smith

Building and Deploying Secure AI: Practical Strategies for Developers

Jim Manico

Machine Learning Foundations for Developers

Michelle Frost

AI Red Teaming: Attacks on LLMs, Agents, and Multimodal Systems

Gary Lopez

Context Is King: Two Days to AI Mastery with MCP & RAG

Aleksander Stensby