When LLMs Meet Cybersecurity: A Systematic Literature Review

• URL: http://arxiv.org/abs/2405.03644v1
• Date: Mon, 6 May 2024 17:07:28 GMT
• Title: When LLMs Meet Cybersecurity: A Systematic Literature Review
• Authors: Jie Zhang, Haoyu Bu, Hui Wen, Yu Chen, Lun Li, Hongsong Zhu,
• Abstract summary: Large language models (LLMs) have opened new avenues across various fields, including cybersecurity. There is a lack of a comprehensive overview of this research area. This study aims to shed light on the extensive potential of LLMs in enhancing cybersecurity practices.
• Score: 9.347716970758604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancements in large language models (LLMs) have opened new avenues across various fields, including cybersecurity, which faces an ever-evolving threat landscape and need for innovative technologies. Despite initial explorations into the application of LLMs in cybersecurity, there is a lack of a comprehensive overview of this research area. This paper bridge this gap by providing a systematic literature review, encompassing an analysis of over 180 works, spanning across 25 LLMs and more than 10 downstream scenarios. Our comprehensive overview addresses three critical research questions: the construction of cybersecurity-oriented LLMs, LLMs' applications in various cybersecurity tasks, and the existing challenges and further research in this area. This study aims to shed light on the extensive potential of LLMs in enhancing cybersecurity practices, and serve as a valuable resource for applying LLMs in this doamin. We also maintain and regularly updated list of practical guides on LLMs for cybersecurity at https://github.com/tmylla/Awesome-LLM4Cybersecurity.

Related papers

• A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment [291.03029298928857]
  This paper introduces the concept of "full-stack" safety to systematically consider safety issues throughout the entire process of LLM training, deployment, and commercialization. Our research is grounded in an exhaustive review of over 800+ papers, ensuring comprehensive coverage and systematic organization of security issues. Our work identifies promising research directions, including safety in data generation, alignment techniques, model editing, and LLM-based agent systems.
  arXiv  Detail & Related papers  (2025-04-22T05:02:49Z)
• LLM-PBE: Assessing Data Privacy in Large Language Models [111.58198436835036]
  Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Despite the critical nature of this issue, there has been no existing literature to offer a comprehensive assessment of data privacy risks in LLMs. Our paper introduces LLM-PBE, a toolkit crafted specifically for the systematic evaluation of data privacy risks in LLMs.
  arXiv  Detail & Related papers  (2024-08-23T01:37:29Z)
• CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions [0.2999888908665658]
  Large Language Models (LLMs) have significantly advanced natural language processing (NLP) capabilities, providing versatile capabilities across various applications. However, their application to complex, domain-specific tasks, such as cyber-security, often faces substantial challenges. In this study, we introduce SecKnowledge and CyberPal.AI to address these challenges and train security-expert LLMs.
  arXiv  Detail & Related papers  (2024-08-17T22:37:39Z)
• The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies [43.65655064122938]
  Large Language Models (LLMs) agents have evolved to perform complex tasks. The widespread applications of LLM agents demonstrate their significant commercial value. However, they also expose security and privacy vulnerabilities. This survey aims to provide a comprehensive overview of the newly emerged privacy and security issues faced by LLM agents.
  arXiv  Detail & Related papers  (2024-07-28T00:26:24Z)
• A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends [78.3201480023907]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding and reasoning tasks. The vulnerability of LVLMs is relatively underexplored, posing potential security risks in daily usage. In this paper, we provide a comprehensive review of the various forms of existing LVLM attacks.
  arXiv  Detail & Related papers  (2024-07-10T06:57:58Z)
• Generative AI and Large Language Models for Cyber Security: All Insights You Need [0.06597195879147556]
  This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs) We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection. We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA.
  arXiv  Detail & Related papers  (2024-05-21T13:02:27Z)
• Large Language Models for Cyber Security: A Systematic Literature Review [14.924782327303765]
  We conduct a comprehensive review of the literature on the application of Large Language Models in cybersecurity (LLM4Security) We observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training.
  arXiv  Detail & Related papers  (2024-05-08T02:09:17Z)
• Prompt Leakage effect and defense strategies for multi-turn LLM interactions [95.33778028192593]
  Leakage of system prompts may compromise intellectual property and act as adversarial reconnaissance for an attacker. We design a unique threat model which leverages the LLM sycophancy effect and elevates the average attack success rate (ASR) from 17.7% to 86.2% in a multi-turn setting. We measure the mitigation effect of 7 black-box defense strategies, along with finetuning an open-source model to defend against leakage attempts.
  arXiv  Detail & Related papers  (2024-04-24T23:39:58Z)
• A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems [47.18371401090435]
  We analyze the security of Large Language Model (LLM) systems, instead of focusing on the individual LLMs. We propose a multi-layer and multi-step approach and apply it to the state-of-art OpenAI GPT4. We found that although the OpenAI GPT4 has designed numerous safety constraints to improve its safety features, these safety constraints are still vulnerable to attackers.
  arXiv  Detail & Related papers  (2024-02-28T19:00:12Z)
• Large Language Models in Cybersecurity: State-of-the-Art [4.990712773805833]
  The rise of Large Language Models (LLMs) has revolutionized our comprehension of intelligence bringing us closer to Artificial Intelligence. This study examines the existing literature, providing a thorough characterization of both defensive and adversarial applications of LLMs within the realm of cybersecurity.
  arXiv  Detail & Related papers  (2024-01-30T16:55:25Z)
• A Survey on Detection of LLMs-Generated Content [97.87912800179531]
  The ability to detect LLMs-generated content has become of paramount importance. We aim to provide a detailed overview of existing detection strategies and benchmarks. We also posit the necessity for a multi-faceted approach to defend against various attacks.
  arXiv  Detail & Related papers  (2023-10-24T09:10:26Z)
• A Comprehensive Overview of Large Language Models [68.22178313875618]
  Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. This article provides an overview of the existing literature on a broad range of LLM-related concepts.
  arXiv  Detail & Related papers  (2023-07-12T20:01:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.