# A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly

**Authors:** Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Zhibo Sun, Yue Zhang  
**arXiv:** [2312.02003](https://arxiv.org/abs/2312.02003)  
**Date:** 2023-12-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2312.02003v3/

## Key points

- Large Language Models (LLMs) like ChatGPT and Bard revolutionize natural language understanding and generation
- LLMs are widely used in search engines, customer support, translation, and have shown deep comprehension, human-like text generation capabilities, contextual awareness, and problem-solving skills
- LLMs have made significant strides in the security community by uncovering vulnerabilities and demonstrating potential in security-related tasks
- The research categorizes findings into "The Good" (beneficial applications), "The Bad" (offensive applications), and "The Ugly" (vulnerabilities and defenses)
- LLMs enhance code security for vulnerability detection and data privacy better than traditional methods but can also be exploited for attacks due to their human-like reasoning abilities
- Areas needing further research include model extraction attacks limited by LLM parameter scale and confidentiality
- Popular LLMs include OpenAI, Google, Meta AI as industry leaders alongside emerging players like Anthropic and Cohere
- Newer models like gpt-4 showcase ongoing innovation with varying parameters indicating increased capabilities but greater computational demands

## Summary

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Large Language Models (LLMs) such as ChatGPT and Bard have revolutionized natural language understanding and generation, showcasing deep comprehension, human-like text generation capabilities, contextual awareness, and problem-solving skills. They are widely utilized in various domains like search engines, customer support, and translation. LLMs have also made significant strides in the security community by uncovering vulnerabilities and demonstrating their potential in security-related tasks. This paper delves into the intersection of LLMs with security and privacy, exploring their positive impacts, potential risks, threats associated with their use, and inherent vulnerabilities. Through a comprehensive literature review, the research categorizes findings into "The Good" (beneficial applications), "The Bad" (offensive applications), and "The Ugly" (vulnerabilities and defenses). One key finding is that LLMs enhance code security for vulnerability detection and data privacy for confidentiality protection better than traditional methods. However, due to their human-like reasoning abilities, they can also be exploited for attacks, especially user-level attacks. The study identifies areas needing further research like model extraction attacks which are limited by LLM parameter scale and confidentiality. The comparison of popular LLMs highlights industry leaders like OpenAI, Google, Meta AI alongside emerging players such as Anthropic and Cohere. Newer models like gpt-4 showcase ongoing innovation in this field with varying parameters indicating increased capabilities but also greater computational demands. In conclusion, this survey aims to establish the current state of security and privacy in the realm of LLMs while pinpointing gaps in knowledge. It sheds light on how LLMs can both bolster cybersecurity through advancements like code vulnerability detection while posing risks through potential attacks leveraging their advanced reasoning abilities. This comprehensive exploration aims to enhance understanding of LLMs' impact on security and privacy domains.