Large Language Models (LLMs) demonstrate impressive capabilities across various fields, yet their increasing use raises critical security concerns. This article reviews recent literature addressing key issues in LLM security, with a focus on accuracy, bias, content detection, and vulnerability to attacks. Issues related to inaccurate or misleading outputs from LLMs is discussed, with emphasis on the implementation from fact-checking methodologies to enhance response reliability. Inherent biases within LLMs are critically examined through diverse evaluation techniques, including controlled input studies and red teaming exercises. A comprehensive analysis of bias mitigation strategies is presented, including approaches from pre-processing interventions to in-training adjustments and post-processing refinements. The article also probes the complexity of distinguishing LLM-generated content from human-produced text, introducing detection mechanisms like DetectGPT and watermarking techniques while noting the limitations of machine learning enabled classifiers under intricate circumstances. Moreover, LLM vulnerabilities, including jailbreak attacks and prompt injection exploits, are analyzed by looking into different case studies and large-scale competitions like HackAPrompt. This review is concluded by retrospecting defense mechanisms to safeguard LLMs, accentuating the need for more extensive research into the LLM security field.
Design Patterns for Securing LLM Agents against Prompt Injections
Invariant Labs；IBM；EPFL（苏黎世联邦理工学院）；ETH Zurich（苏黎世联邦理工学院）；Swisscom（瑞士通信）；Google（谷歌）；ETH AI Center（苏黎世联邦理工学院人工智能中心）；AppliedAI Institute for Europe（欧洲应用AI研究院）；Microsoft（微软）；Lakera
'They're all about pushing the products and shiny things rather than fundamental security' Mapping Socio-technical Challenges in Securing the Smart Home
Advancing LLM-Based Security Automation with Customized Group Relative Policy Optimization for Zero-Touch Networks
National Engineering Research Center for Mobile Network Technologies, Beijing University of Posts and Telecommunications（移动网络技术国家工程研究中心，北京邮电大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Department of Electronic Engineering, Kyung Hee University（韩国庆熙大学电子工程系）
