Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift

• URL: http://arxiv.org/abs/2406.18844v4
• Date: Mon, 16 Dec 2024 06:59:33 GMT
• Title: Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift
• Authors: Siyuan Liang, Jiawei Liang, Tianyu Pang, Chao Du, Aishan Liu, Mingli Zhu, Xiaochun Cao, Dacheng Tao,
• Abstract summary: This paper explores backdoor attacks in LVLM instruction tuning across mismatched training and testing domains.<n>We introduce a new evaluation dimension, backdoor domain generalization, to assess attack robustness.<n>We propose a multimodal attribution backdoor attack (MABA) that injects domain-agnostic triggers into critical areas.
• Score: 104.76588209308666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instruction tuning enhances large vision-language models (LVLMs) but increases their vulnerability to backdoor attacks due to their open design. Unlike prior studies in static settings, this paper explores backdoor attacks in LVLM instruction tuning across mismatched training and testing domains. We introduce a new evaluation dimension, backdoor domain generalization, to assess attack robustness under visual and text domain shifts. Our findings reveal two insights: (1) backdoor generalizability improves when distinctive trigger patterns are independent of specific data domains or model architectures, and (2) the competitive interaction between trigger patterns and clean semantic regions, where guiding the model to predict triggers enhances attack generalizability. Based on these insights, we propose a multimodal attribution backdoor attack (MABA) that injects domain-agnostic triggers into critical areas using attributional interpretation. Experiments with OpenFlamingo, Blip-2, and Otter show that MABA significantly boosts the attack success rate of generalization by 36.4%, achieving a 97% success rate at a 0.2% poisoning rate. This study reveals limitations in current evaluations and highlights how enhanced backdoor generalizability poses a security threat to LVLMs, even without test data access.

Related papers

• Lie Detector: Unified Backdoor Detection via Cross-Examination Framework [68.45399098884364]
  We propose a unified backdoor detection framework in the semi-honest setting. Our method achieves superior detection performance, improving accuracy by 5.4%, 1.6%, and 11.9% over SoTA baselines. Notably, it is the first to effectively detect backdoors in multimodal large language models.
  arXiv  Detail & Related papers  (2025-03-21T06:12:06Z)
• Neural Antidote: Class-Wise Prompt Tuning for Purifying Backdoors in Pre-trained Vision-Language Models [42.81731204702258]
  Class-wise Backdoor Prompt Tuning (CBPT) is an efficient and effective method that operates on the text prompts to indirectly purify poisoned Vision-Language Models (VLMs) CBPT significantly mitigates backdoor threats while preserving model utility, e.g. an average Clean Accuracy (CA) of 58.86% and an Attack Success Rate (ASR) of 0.39% across seven mainstream backdoor attacks.
  arXiv  Detail & Related papers  (2025-02-26T16:25:15Z)
• ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models [55.93380086403591]
  Generative large language models are vulnerable to backdoor attacks. $textitELBA-Bench$ allows attackers to inject backdoor through parameter efficient fine-tuning. $textitELBA-Bench$ provides over 1300 experiments.
  arXiv  Detail & Related papers  (2025-02-22T12:55:28Z)
• Proactive Adversarial Defense: Harnessing Prompt Tuning in Vision-Language Models to Detect Unseen Backdoored Images [0.0]
  Backdoor attacks pose a critical threat by embedding hidden triggers into inputs, causing models to misclassify them into target labels. We introduce a groundbreaking method to detect unseen backdoored images during both training and inference. Our approach trains learnable text prompts to differentiate clean images from those with hidden backdoor triggers.
  arXiv  Detail & Related papers  (2024-12-11T19:54:14Z)
• BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks on Large Language Models [27.59116619946915]
  We introduce textitBackdoorLLM, the first comprehensive benchmark for studying backdoor attacks on Generative Large Language Models. textitBackdoorLLM features: 1) a repository of backdoor benchmarks with a standardized training pipeline, 2) diverse attack strategies, including data poisoning, weight poisoning, hidden state attacks, and chain-of-thought attacks, and 3) extensive evaluations with over 200 experiments on 8 attacks across 7 scenarios and 6 model architectures.
  arXiv  Detail & Related papers  (2024-08-23T02:21:21Z)
• T2IShield: Defending Against Backdoors on Text-to-Image Diffusion Models [70.03122709795122]
  We propose a comprehensive defense method named T2IShield to detect, localize, and mitigate backdoor attacks. We find the "Assimilation Phenomenon" on the cross-attention maps caused by the backdoor trigger. For backdoor sample detection, T2IShield achieves a detection F1 score of 88.9$%$ with low computational cost.
  arXiv  Detail & Related papers  (2024-07-05T01:53:21Z)
• SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks [53.28390057407576]
  Modern NLP models are often trained on public datasets drawn from diverse sources. Data poisoning attacks can manipulate the model's behavior in ways engineered by the attacker. Several strategies have been proposed to mitigate the risks associated with backdoor attacks.
  arXiv  Detail & Related papers  (2024-05-19T14:50:09Z)
• 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)
• Model X-ray:Detecting Backdoored Models via Decision Boundary [62.675297418960355]
  Backdoor attacks pose a significant security vulnerability for deep neural networks (DNNs) We propose Model X-ray, a novel backdoor detection approach based on the analysis of illustrated two-dimensional (2D) decision boundaries. Our approach includes two strategies focused on the decision areas dominated by clean samples and the concentration of label distribution.
  arXiv  Detail & Related papers  (2024-02-27T12:42:07Z)
• BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning [85.2564206440109]
  This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses. We introduce the emphtoolns attack, which is resistant to backdoor detection and model fine-tuning defenses.
  arXiv  Detail & Related papers  (2023-11-20T02:21:49Z)
• Demystifying Poisoning Backdoor Attacks from a Statistical Perspective [35.30533879618651]
  Backdoor attacks pose a significant security risk due to their stealthy nature and potentially serious consequences. This paper evaluates the effectiveness of any backdoor attack incorporating a constant trigger. Our derived understanding applies to both discriminative and generative models.
  arXiv  Detail & Related papers  (2023-10-16T19:35:01Z)
• Rethinking Backdoor Attacks [122.1008188058615]
  In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against such attacks typically involves viewing these inserted examples as outliers in the training set and using techniques from robust statistics to detect and remove them. We show that without structural information about the training data distribution, backdoor attacks are indistinguishable from naturally-occurring features in the data.
  arXiv  Detail & Related papers  (2023-07-19T17:44:54Z)
• Rethinking the Trigger-injecting Position in Graph Backdoor Attack [7.4968235623939155]
  Backdoor attacks have been demonstrated as a security threat for machine learning models. In this paper, we study two trigger-injecting strategies for backdoor attacks on Graph Neural Networks (GNNs) Our results show that, generally, LIAS performs better, and the differences between the LIAS and MIAS performance can be significant.
  arXiv  Detail & Related papers  (2023-04-05T07:50:05Z)
• Mitigating Backdoors in Federated Learning with FLD [7.908496863030483]
  Federated learning allows clients to collaboratively train a global model without uploading raw data for privacy preservation. This feature has recently been found responsible for federated learning's vulnerability in the face of backdoor attacks. We propose Federated Layer Detection (FLD), a novel model filtering approach for effectively defending against backdoor attacks.
  arXiv  Detail & Related papers  (2023-03-01T07:54:54Z)
• Understanding Impacts of Task Similarity on Backdoor Attack and Detection [17.5277044179396]
  We use similarity metrics in multi-task learning to define the backdoor distance (similarity) between the primary task and the backdoor task. We then analyze existing stealthy backdoor attacks, revealing that most of them fail to effectively reduce the backdoor distance. We then design a new method, called TSA attack, to automatically generate a backdoor model under a given distance constraint.
  arXiv  Detail & Related papers  (2022-10-12T18:07:39Z)
• On the Effectiveness of Adversarial Training against Backdoor Attacks [111.8963365326168]
  A backdoored model always predicts a target class in the presence of a predefined trigger pattern. In general, adversarial training is believed to defend against backdoor attacks. We propose a hybrid strategy which provides satisfactory robustness across different backdoor attacks.
  arXiv  Detail & Related papers  (2022-02-22T02:24:46Z)

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.