Adversarial attacks and defenses for large language models (LLMs): methods, frameworks & challenges

Large language models (LLMs) have exhibited remarkable efficacy and proficiency in a wide array of NLP endeavors. Nevertheless, concerns are growing rapidly regarding the security and vulnerabilities linked to the adoption and incorporation of LLM. In this work, a systematic study focused on the most up-to-date attack and defense frameworks for the LLM is presented. This work delves into the intricate landscape of adversarial attacks on language models (LMs) and presents a thorough problem formulation. It covers a spectrum of attack enhancement techniques and also addresses methods for strengthening LLMs. This study also highlights challenges in the field, such as the assessment of offensive or defensive performance, defense and attack transferability, high computational requirements, embedding space size, and perturbation. This survey encompasses more than 200 recent papers concerning adversarial attacks and techniques. By synthesizing a broad array of attack techniques, defenses, and challenges, this paper contributes to the ongoing discourse on securing LM against adversarial threats.