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

We levelled up the threat modeling war game. Engaged in CTF-style challenges, your team will battle for control over an offshore wind turbine park.

The level of this training is Beginner/Intermediate. Participants who are new to threat modeling are advised to follow our self-paced Threat Modeling Introduction training (which is about 2 hours and is included in this training).

As highly skilled professionals with years of experience under our belts, we're intimately familiar with the gap between academic knowledge of threat modeling and real-world practice. To minimize that gap, we have developed practical use cases, based on real-world projects. Each use case includes a description of the environment, together with questions and templates to build a threat model.

Students will be challenged in groups of 3 to 4 people to perform the different stages of threat modeling:

• Diagram techniques applied on a travel booking service
• Threat model a cloud-based update service for an IoT kiosk
• Create an attack tree against a nuclear research facility
• Create a SOC Risk Based Alerting system with MITRE ATT&CK
• Mitigate threats in a payment service build with microservices and S3 buckets
• Apply data protection by design and default on a loyalty app
• Apply the OWASP Threat Modeling Playbook on agile development
• Threat modeling the CI/CD pipeline
• Battle for control over "Zwarte Wind", an offshore wind turbine park

After each hands-on exercise, the results are discussed, and students receive a documented solution. All participants get our Threat Modeling Playbook to improve you threat modeling practice, and a one-year access to our online threat modeling learning platform.

As part of this training, you will be asked to create and submit your own threat model, on which you will get feedback. One month after the training we organize an individual coaching session with all the participants.


Course Syllabus/Outline

Agile Whiteboard Hacking - aka Hands-on Threat Modeling - Course Outline

Threat modeling introduction
• Threat modeling in a secure development lifecycle
• What is threat modeling?
• Why perform threat modeling?
• Threat modeling stages
• Different threat modeling methodologies
• Document a threat model
Diagrams – what are you building?
• Understanding context
• Doomsday scenarios
• Data flow diagrams
• Trust boundaries
• Sequence and state diagrams
• Advanced diagrams
• Hands-on: Diagram techniques applied on a travel booking service
Identifying threats – what can go wrong?
• STRIDE introduction
• STRIDE threats
• Hands-on: Threat model a cloud-based update service for an IoT kiosk
• Attack trees
• Hands-on: Create an attack tree against a nuclear research facility
• Attack libraries
• MITRE ATT&CK
• Hands-on: Create a SOC Risk Based Alerting system with MITRE ATT&CK

Addressing each threat
• How to address threats
• Mitigation patterns
• Value of standard mitigations
• Setting priorities through risk calculation
• Risk management
• Threat agents
• The mitigation process
• Hands-on: Mitigate threats in a payment service build with microservices and S3 buckets
Threat modeling and compliance
• How to marry threat modeling with compliance
• GDPR and Privacy by design
• Privacy threats
• LINDUNN and Mitigating privacy threats
• Threat modeling medical devices
• Threat modeling Industrial Control Systems (IEC 62443)
• Threat Assessment and Remediation Analysis for automotive (TARA, SAE 21434)
• Mapping threat modeling on compliance frameworks
• Hands-on: Apply data protection by design and default on a loyalty app
Advanced threat modeling
• Typical steps and variations
• Validation threat models
• Effective threat model workshops
• Communicating threat models
• Agile and DevOps threat modeling
• Improving your practice with the Threat Modeling Playbook
• Scaling up threat modeling
• Hands-on: Apply the OWASP Threat Modeling Playbook on agile development
• Hands-on: Threat modeling the CI/CD pipeline
Threat modeling resources
• Open-Source tools
• Commercial tools
• General tools
• Threat modeling tools compared
Examination
• Hands-on examination
• Grading and certification
Battle for control over "Zwarte Wind", an offshore wind turbine park
Red team versus Blue team battle for control over an offshore wind turbine park

Review session (online coaching session after 1 month)
• Hand-in of your own threat model
• Individual feedback on your threat model
• Review session


Agile Whiteboard Hacking – aka Hands-on Threat Modeling

AI/ML

Application Security

Architecture

Cloud Security

Hacking

Privacy

SDLC

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

Day 1: Foundations — Servers & Knowledge

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

MCP Apps: Creating interactive AI-powered applications

MCP Clients: Building your own host applications

Deployment options: Remote MCPs, Cloud providers and hosting alternatives

Preparing for production: Security considerations and optimization strategies

Final exercise: Building an MCP App that exposes your knowledge base

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.



Bring your laptop with your preferred IDE and either Python or Node installed. We recommend an AI-coding agent like Claude Code, Cursor or GitHub Copilot, but not required!


 
You need access to an LLM provider with an embedding model like OpenAI or Azure Foundry, but you can also use Ollama if you would like to run everything locally and free.

NDC { Manchester }

Workshops

09:00 - 17:00 (UTC±00)

Agile Whiteboard Hacking – aka Hands-on Threat Modeling

Georges Bolssens

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

Gary Lopez

Amanda Minnich

Context Is King: Building AI Apps with MCP & RAG

Aleksander Stensby