Motivation

Large Language Models (LLMs) are increasingly deployed in research and real-world applications, where they interact with sensitive data, external tools, and human users. Despite their capabilities, LLMs introduce novel security and privacy challenges, including prompt injection, data leakage, and unintended disclosure of sensitive information. These risks stem from the fundamental design of LLMs, which interpret natural language instructions without reliable mechanisms to distinguish trusted from adversarial inputs. As a result, ensuring the resilience, robustness, and trustworthiness of LLM-based systems has become a critical research challenge.

This workshop aims to provide an accessible overview of how LLMs work, introduce common attack scenarios, discuss privacy risks, and present methods for adding safeguards and protective mechanisms when building LLM-based systems.
Workshop Goals

Participants will:

Understand how LLMs work (internals and architecture)
Understand security, privacy, and reliability risks
Perform hands-on attacks (prompt injection, data leakage)
Learn how to design secure, privacy-aware LLM systems

Target Audience

General audience: technical insights into LLMs and their vulnerability
Developers, IT professionals: hands-on building secure LLM apps