A Survey of Backdoor Attacks and Defenses on Large Language Models: Implications for Security Measures

• URL: http://arxiv.org/abs/2406.06852v4
• Date: Thu, 12 Sep 2024 00:27:06 GMT
• Title: A Survey of Backdoor Attacks and Defenses on Large Language Models: Implications for Security Measures
• Authors: Shuai Zhao, Meihuizi Jia, Zhongliang Guo, Leilei Gan, Xiaoyu Xu, Xiaobao Wu, Jie Fu, Yichao Feng, Fengjun Pan, Luu Anh Tuan,
• Abstract summary: Large Language Models (LLMs), which bridge the gap between human language understanding and complex problem-solving, achieve state-of-the-art performance on several NLP tasks. Research has demonstrated that language models are susceptible to potential security vulnerabilities, particularly in backdoor attacks. This paper presents a novel perspective on backdoor attacks for LLMs by focusing on fine-tuning methods.
• Score: 28.604839267949114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs), which bridge the gap between human language understanding and complex problem-solving, achieve state-of-the-art performance on several NLP tasks, particularly in few-shot and zero-shot settings. Despite the demonstrable efficacy of LLMs, due to constraints on computational resources, users have to engage with open-source language models or outsource the entire training process to third-party platforms. However, research has demonstrated that language models are susceptible to potential security vulnerabilities, particularly in backdoor attacks. Backdoor attacks are designed to introduce targeted vulnerabilities into language models by poisoning training samples or model weights, allowing attackers to manipulate model responses through malicious triggers. While existing surveys on backdoor attacks provide a comprehensive overview, they lack an in-depth examination of backdoor attacks specifically targeting LLMs. To bridge this gap and grasp the latest trends in the field, this paper presents a novel perspective on backdoor attacks for LLMs by focusing on fine-tuning methods. Specifically, we systematically classify backdoor attacks into three categories: full-parameter fine-tuning, parameter-efficient fine-tuning, and no fine-tuning Based on insights from a substantial review, we also discuss crucial issues for future research on backdoor attacks, such as further exploring attack algorithms that do not require fine-tuning, or developing more covert attack algorithms.

Related papers

• Mitigating Backdoor Threats to Large Language Models: Advancement and Challenges [46.032173498399885]
  Large Language Models (LLMs) have significantly impacted various domains, including Web search, healthcare, and software development. As these models scale, they become more vulnerable to cybersecurity risks, particularly backdoor attacks.
  arXiv  Detail & Related papers  (2024-09-30T06:31:36Z)
• Large Language Models are Good Attackers: Efficient and Stealthy Textual Backdoor Attacks [10.26810397377592]
  We introduce the Efficient and Stealthy Textual backdoor attack method, EST-Bad, leveraging Large Language Models (LLMs) Our EST-Bad encompasses three core strategies: optimizing the inherent flaw of models as the trigger, stealthily injecting triggers with LLMs, and meticulously selecting the most impactful samples for backdoor injection.
  arXiv  Detail & Related papers  (2024-08-21T12:50:23Z)
• MEGen: Generative Backdoor in Large Language Models via Model Editing [56.46183024683885]
  Large language models (LLMs) have demonstrated remarkable capabilities. Their powerful generative abilities enable flexible responses based on various queries or instructions. This paper proposes an editing-based generative backdoor, named MEGen, aiming to create a customized backdoor for NLP tasks with the least side effects.
  arXiv  Detail & Related papers  (2024-08-20T10:44:29Z)
• Revisiting Backdoor Attacks against Large Vision-Language Models [76.42014292255944]
  This paper empirically examines the generalizability of backdoor attacks during the instruction tuning of LVLMs. We modify existing backdoor attacks based on the above key observations. This paper underscores that even simple traditional backdoor strategies pose a serious threat to LVLMs.
  arXiv  Detail & Related papers  (2024-06-27T02:31:03Z)
• TuBA: Cross-Lingual Transferability of Backdoor Attacks in LLMs with Instruction Tuning [63.481446315733145]
  Cross-lingual backdoor attacks against multilingual large language models (LLMs) are under-explored. Our research focuses on how poisoning the instruction-tuning data for one or two languages can affect the outputs for languages whose instruction-tuning data were not poisoned. Our method exhibits remarkable efficacy in models like mT5 and GPT-4o, with high attack success rates, surpassing 90% in more than 7 out of 12 languages.
  arXiv  Detail & Related papers  (2024-04-30T14:43:57Z)
• Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Review [15.179940846141873]
  Applicating third-party data and models has become a new paradigm for language modeling in NLP. backdoor attacks can induce the model to exhibit expected behaviors through specific triggers. There is still no systematic and comprehensive review to reflect the security challenges, attacker's capabilities, and purposes.
  arXiv  Detail & Related papers  (2023-09-12T08:48:38Z)
• A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks [28.1095109118807]
  Large Language Models (LLMs) are poised to offer efficient and intelligent services for future mobile communication networks. LLMs may be exposed to maliciously manipulated training data and processing, providing an opportunity for attackers to embed a hidden backdoor into the model. Backdoor attacks are particularly concerning within communication networks where reliability and security are paramount.
  arXiv  Detail & Related papers  (2023-08-28T07:31:43Z)
• A Survey on Backdoor Attack and Defense in Natural Language Processing [18.29835890570319]
  We conduct a comprehensive review of backdoor attacks and defenses in the field of NLP. We summarize benchmark datasets and point out the open issues to design credible systems to defend against backdoor attacks.
  arXiv  Detail & Related papers  (2022-11-22T02:35:12Z)
• Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution [57.51117978504175]
  Recent studies show that neural natural language processing (NLP) models are vulnerable to backdoor attacks. Injected with backdoors, models perform normally on benign examples but produce attacker-specified predictions when the backdoor is activated. We present invisible backdoors that are activated by a learnable combination of word substitution.
  arXiv  Detail & Related papers  (2021-06-11T13:03:17Z)
• Backdoor Learning: A Survey [75.59571756777342]
  Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs) Backdoor learning is an emerging and rapidly growing research area. This paper presents the first comprehensive survey of this realm.
  arXiv  Detail & Related papers  (2020-07-17T04:09:20Z)

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.