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

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WorkDifferentWithAI.com Academic Paper Alert!

Written by Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Eric Sun, Yue Zhang

Category: AI for IT

Article Section: AI Development and Operations; AI-Assisted Programming

Publication Date: 2023-12-04

SEO Description: Comprehensive review of LLMs’ role in enhancing and threatening cybersecurity with security and privacy implications explored.

Keywords

Large Language Models, Security, Privacy, Vulnerabilities, Cybersecurity

AI-Generated Paper Summary

Generated by Ethical AI Researcher GPT

The paper titled “A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly” by Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Eric Sun, and Yue Zhang is a comprehensive exploration of the security and privacy implications of Large Language Models (LLMs) like ChatGPT and Bard. The authors systematically categorize the impact of LLMs into three primary sections: The Good (beneficial applications), The Bad (offensive applications), and The Ugly (vulnerabilities and defenses)​​.

Author Caliber: The authors are affiliated with Drexel University, indicating a strong academic background. Their research contributes significantly to the understanding of LLMs in the context of cybersecurity.

Novelty & Merit:

  1. Comprehensive review of LLM applications in security and privacy.
  2. Detailed categorization of LLM impacts into beneficial, offensive, and vulnerability aspects.
  3. Identification of new areas requiring research, particularly in model and parameter extraction attacks and safe instruction tuning.

Commercial Applications:

  1. Enhancing code and data security in various industries.
  2. Utilizing LLMs for secure coding, test case generation, and vulnerability detection.
  3. Applying LLMs for ensuring data integrity, confidentiality, reliability, and traceability.

Findings and Conclusions:

  1. LLMs have a predominantly positive impact on the security community, enhancing both code security and data security more effectively than traditional methods.
  2. LLMs can be used offensively in various attacks, especially user-level attacks like misinformation, social engineering, and scientific misconduct.
  3. Key vulnerabilities in LLMs include AI Model Inherent Vulnerabilities (like data poisoning and backdoor attacks) and Non-AI Model Inherent Vulnerabilities (such as prompt injection and side channels).
  4. Defense strategies for LLMs focus on model architecture improvements, corpora cleaning, and optimization methods during training, and instruction pre-processing and malicious detection during inference.
  5. Most researchers agree that LLMs outperform state-of-the-art methods for securing code or data, but they also note the prevalence of user-level attacks due to LLMs’ human-like reasoning abilities​​.

Author’s Abstract

Large Language Models (LLMs), such as GPT-3 and BERT, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text generation capabilities, contextual awareness, and robust problem-solving skills, making them invaluable in various domains (e.g., search engines, customer support, translation). In the meantime, LLMs have also gained traction in the security community, revealing security vulnerabilities and showcasing their potential in security-related tasks. This paper explores the intersection of LLMs with security and privacy. Specifically, we investigate how LLMs positively impact security and privacy, potential risks and threats associated with their use, and inherent vulnerabilities within LLMs. Through a comprehensive literature review, the paper categorizes findings into “The Good” (beneficial LLM applications), “The Bad” (offensive applications), and “The Ugly” (vulnerabilities and their defenses). We have some interesting findings. For example, LLMs have proven to enhance code and data security, outperforming traditional methods. However, they can also be harnessed for various attacks (particularly user-level attacks) due to their human-like reasoning abilities. We have identified areas that require further research efforts. For example, research on model and parameter extraction attacks is limited and often theoretical, hindered by LLM parameter scale and confidentiality. Safe instruction tuning, a recent development, requires more exploration. We hope that our work can shed light on the LLMs’ potential to both bolster and jeopardize cybersecurity.

Read the full paper here

Last updated on December 11th, 2023.