Recent Advances in Attack and Defense Approaches of Large Language Models
It addresses safety and reliability concerns for LLM users and developers by summarizing existing work, but is incremental as it primarily reviews and synthesizes rather than introduces new methods.
This paper reviews current research on vulnerabilities and threats in Large Language Models (LLMs), analyzing attack vectors and evaluating the effectiveness of defense mechanisms to identify gaps and propose future directions for enhancing security.
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.