# Security and Privacy Challenges of Large Language Models: A Survey

**Authors:** Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu  
**arXiv:** [2402.00888](https://arxiv.org/abs/2402.00888)  
**Date:** 2024-01-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2402.00888v1/

## Key points

- Large Language Models (LLMs) are powerful tools used in text generation, language translation, and question-answering due to their ability to analyze complex linguistic patterns and provide contextually relevant responses.
- The increasing popularity of LLMs poses security and privacy risks, especially in domains like transportation, education, and healthcare.
- This survey focuses on the security and privacy challenges faced by LLMs in training data and user interactions across various domains.
- The authors provide a comprehensive analysis of the latest developments in privacy and security concerns surrounding LLMs, comparing their work with existing surveys and empirical studies.
- The paper outlines the architecture of LLMs, highlighting their extensive parameter sizes and intelligent learning capabilities.
- Mitigation techniques for different types of attacks on LLMs are discussed along with application-specific risks in different domains.
- Existing research gaps in this area are identified while proposing future research directions to address unexplored challenges.

## Summary

Large Language Models (LLMs) have emerged as powerful tools in various fields such as text generation, language translation, and question-answering due to their ability to analyze complex linguistic patterns and provide contextually relevant responses. However, with their increasing popularity comes the risk of security and privacy attacks. This survey delves into the security and privacy challenges faced by LLMs in both training data and user interactions across different domains like transportation, education, and healthcare. In this paper, the authors contribute a comprehensive analysis of the latest developments in privacy and security concerns surrounding LLMs. They compare their work with existing surveys and empirical studies to provide a systematic discussion on representative issues and defense mechanisms for LLMs. Unlike previous surveys, this study focuses on recent advancements in security and privacy for LLMs, offering insights into emerging research areas and novel techniques within this domain. The paper outlines the architecture of LLMs, highlighting their extensive parameter sizes and intelligent learning capabilities. It explains the multi-step workflow involved in pretraining the model with a large dataset before fine-tuning it for specific tasks or domains. The authors discuss how LLMs process input text through deep neural networks with attention mechanisms to generate coherent output based on learned representations. Furthermore, the survey categorizes different vulnerabilities of LLMs and explores prevalent security and privacy attacks targeting these models. Mitigation techniques for various types of attacks are discussed along with application-specific risks in different domains. The study also identifies existing research gaps in this area while proposing future research directions to address unexplored challenges. Overall, this paper offers a timely review of security and privacy issues surrounding Large Language Models, providing valuable insights into potential attack mitigation strategies and highlighting areas for further exploration in this rapidly evolving field.