Exploring the Impact of Quantization on LLM Security Against Prompt Injection - Rodrigues, Rafael Araújo; Garcia, Luan Fonseca; Zorzo, Avelino Francisco; Oliveira, Ewerton de; Paula, Thomas (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS), 2025)
Large Language Models (LLMs) face challenges in efficiency and security. Quantization improves performance but its effect on adversarial robustness is unclear against prompt injection. We propose an experimental setup to investigate how post-training quantization may influence LLM vulnerability to prompt injections, aiming to enlighten the trade-offs between efficiency and security of quantization. The experiments are currently in progress, and we intend to stimulate an open debate on this topic.