Recent Advances in Attack and Defense Approaches of Large Language Models

TL;DR

This paper reviews recent research on vulnerabilities and defenses of Large Language Models, analyzing attack methods, defense strategies, and identifying gaps to guide future security improvements.

Contribution

It provides a comprehensive overview of current attack and defense techniques for LLMs, highlighting research gaps and proposing future directions.

Findings

Analysis of recent attack vectors and model weaknesses

Evaluation of effectiveness of defense mechanisms

Identification of research gaps in LLM security

Abstract

Large Language Models (LLMs) have revolutionized artificial intelligence and machine learning through their advanced text processing and generating capabilities. However, their widespread deployment has raised significant safety and reliability concerns. Established vulnerabilities in deep neural networks, coupled with emerging threat models, may compromise security evaluations and create a false sense of security. Given the extensive research in the field of LLM security, we believe that summarizing the current state of affairs will help the research community better understand the present landscape and inform future developments. This paper reviews current research on LLM vulnerabilities and threats, and evaluates the effectiveness of contemporary defense mechanisms. We analyze recent studies on attack vectors and model weaknesses, providing insights into attack mechanisms and the evolving threat landscape. We also examine current defense strategies, highlighting their strengths and limitations. By contrasting advancements in attack and defense methodologies, we identify research gaps and propose future directions to enhance LLM security. Our goal is to advance the understanding of LLM safety challenges and guide the development of more robust security measures.