LLMs Under Attack: Understanding the Adversarial Mindset

With Large Language Models (LLMs) powering critical applications, adversarial threats present urgent challenges to their safety and reliability. This tutorial explores adversarial threats against LLMs by covering foundational concepts, identifying key security implications, examining specific attack vectors (such as data poisoning, evasion techniques, and prompt-engineering vulnerabilities), and highlighting LLMs' dual roles as both targets and enablers of malicious activity. We critically assess current defensive approaches, discuss recent criticisms regarding detection reliability and ethical considerations, and outline key open research challenges. Attendees will gain practical insights into anticipating and mitigating adversarial threats to secure the deployment and application of LLM systems.