Moritz Krause
Security Engineer @ FullCyrcle Security
Moritz Krause has been working in software development since 2010, with a strong focus on building and securing web applications. After completing a degree in computer science and working as a software engineer for many years, he shifted his focus toward cybersecurity in 2024. His current work centers on DevSecOps, backend development, AI security, and threat modeling. Combining deep technical expertise with a strong understanding of the secure software development lifecycle (SDLC), Moritz explores how AI can be leveraged to enhance security practices across modern development environments.
Session
LLMs are showing up in production applications everywhere (chatbots, support widgets, internal tools, CMS plugins), often without addressing LLM-specific risks: prompt injection, RAG poisoning, jailbreaks, and data leaks. This 4-hour hands-on workshop shows what can go wrong and how to make these weaknesses significantly harder to find and exploit.
Participants will attack a deliberately vulnerable chatbot application running in a Docker lab, performing direct and indirect prompt injections, extracting system prompts, jailbreaking safety guardrails, and more. In the second half, we shift from offense to defense: using agentic coding tools to automatically detect vulnerabilities, generate secure input validation, and wire security checks into a CI/CD pipeline.
By the end of the session, attendees will be able to:
- Name and execute the top attack vectors against LLM applications
- Test their own LLM-powered apps for injection vulnerabilities
- Implement practical mitigation strategies
- Integrate automated security checks into CI/CD
Takeaways: an understanding of LLM vulnerabilities, hands-on familiarity with the top attack techniques against LLM applications, a practical framework for mitigating them, and how to embed LLM-specific security checks into a CI/CD workflow
Target audience: developers building AI features into existing applications and DevSecOps engineers deploying LLMs safely.
Prerequisites: Basic Docker knowledge; interest in application or LLM security. No prior ML background required.