A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning

• URL: http://arxiv.org/abs/2604.03995v1
• Date: Sun, 05 Apr 2026 06:32:08 GMT
• Title: A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning
• Abstract summary: Multi-Modal Typography is a systematic study examining how typographic attacks adversely influence multi-modal large language models.<n>We analyze the interactions between audio, visual, and text perturbations and reveal that coordinated multi-modal attack creates a significantly more potent threat than single-modality attacks.
• Score: 6.442194752385315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As audio-visual multi-modal large language models (MLLMs) are increasingly deployed in safety-critical applications, understanding their vulnerabilities is crucial. To this end, we introduce Multi-Modal Typography, a systematic study examining how typographic attacks across multiple modalities adversely influence MLLMs. While prior work focuses narrowly on unimodal attacks, we expose the cross-modal fragility of MLLMs. We analyze the interactions between audio, visual, and text perturbations and reveal that coordinated multi-modal attack creates a significantly more potent threat than single-modality attacks (attack success rate = $83.43\%$ vs $34.93\%$).Our findings across multiple frontier MLLMs, tasks, and common-sense reasoning and content moderation benchmarks establishes multi-modal typography as a critical and underexplored attack strategy in multi-modal reasoning. Code and data will be publicly available.

Related papers

• Forbid Your Attention: Fooling Multimodal Large Language Models by Selectively Removing Intrinsic Focus in Spectral Domain [94.64605746540472]
  Multimodal large language models (MLLMs) have extended the capability of large language models (LLMs) to process more contextual multimodal information.<n>Recent studies reveal that MLLMs remain highly vulnerable to adversarial inputs, especially those targeting visual components.<n>We propose a novel phase-aware adversarial attack framework that explicitly restricts adversarial perturbations to structure-relevant phase regions.
  arXiv  Detail & Related papers  (2026-09-01T06:34:32Z)
• Adversarial Attacks on Multimodal Large Language Models: A Comprehensive Survey [0.21847754147782886]
  Multimodal large language models (MLLMs) integrate information from multiple modalities such as text, images, audio, and video, enabling complex capabilities such as visual question answering and audio translation.<n>This survey provides a comprehensive and systematic analysis of adversarial threats to MLLMs.<n>We introduce a taxonomy that organizes adversarial attacks according to attacker objectives, unifying diverse attack surfaces across modalities and deployment settings.<n>We also present a vulnerability-centric analysis that links integrity attacks, safety and jailbreak failures, control and instruction hijacking, and training-time poisoning to shared architectural and representational weaknesses in multimodal systems.
  arXiv  Detail & Related papers  (2026-03-30T00:16:31Z)
• When Alignment Fails: Multimodal Adversarial Attacks on Vision-Language-Action Models [75.16145284285456]
  We introduce VLA-Fool, a comprehensive study of multimodal adversarial robustness in embodied VLA models under both white-box and black-box settings.<n>We develop the first automatically crafted and semantically guided prompting framework.<n> Experiments on the LIBERO benchmark reveal that even minor multimodal perturbations can cause significant behavioral deviations.
  arXiv  Detail & Related papers  (2025-11-20T10:14:32Z)
• Sequential Comics for Jailbreaking Multimodal Large Language Models via Structured Visual Storytelling [11.939828002077482]
  Multimodal large language models (MLLMs) exhibit remarkable capabilities but remain susceptible to jailbreak attacks.<n>We introduce a novel method that leverages sequential comic-style visual narratives to circumvent safety alignments in state-of-the-art MLLMs.<n>Our approach achieves an average attack success rate of 83.5%, surpassing prior state-of-the-art by 46%.
  arXiv  Detail & Related papers  (2025-10-16T18:30:26Z)
• Watch, Listen, Understand, Mislead: Tri-modal Adversarial Attacks on Short Videos for Content Appropriateness Evaluation [1.0012740151280692]
  This paper introduces a framework for evaluating the tri-modal safety of Multimodal Large Language Models (MLLMs)<n>We present the Short-Video Multimodal Adversarial dataset, comprising diverse short-form videos with human-guided synthetic adversarial attacks.<n> Extensive experiments on state-of-the-art MLLMs reveal significant vulnerabilities with high Attack Success Rates (ASR)
  arXiv  Detail & Related papers  (2025-07-16T07:02:15Z)
• Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models [25.23931196918614]
  Multimodal large language models (MLLMs) bridge the gap between audio-visual and natural language processing.<n>Despite the superior performance of MLLMs, the scarcity of high-quality audio-visual training data and computational resources necessitates the utilization of third-party data and open-source MLLMs.<n> Empirical studies demonstrate that the latest MLLMs can be manipulated to produce malicious or harmful content.
  arXiv  Detail & Related papers  (2025-06-13T07:22:36Z)
• Align is not Enough: Multimodal Universal Jailbreak Attack against Multimodal Large Language Models [83.80177564873094]
  We propose a unified multimodal universal jailbreak attack framework.<n>We evaluate the undesirable context generation of MLLMs like LLaVA, Yi-VL, MiniGPT4, MiniGPT-v2, and InstructBLIP.<n>This study underscores the urgent need for robust safety measures in MLLMs.
  arXiv  Detail & Related papers  (2025-06-02T04:33:56Z)
• Con Instruction: Universal Jailbreaking of Multimodal Large Language Models via Non-Textual Modalities [76.9327488986162]
  Existing attacks against multimodal language models (MLLMs) primarily communicate instructions through text accompanied by adversarial images.<n>We exploit the capabilities of MLLMs to interpret non-textual instructions, specifically, adversarial images or audio generated by our novel method, Con Instruction.<n>Our method achieves the highest attack success rates, reaching 81.3% and 86.6% on LLaVA-v1.5 (13B)
  arXiv  Detail & Related papers  (2025-05-31T13:11:14Z)
• Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation [88.78166077081912]
  We introduce a multimodal unlearning benchmark, UnLOK-VQA, and an attack-and-defense framework to evaluate methods for deleting specific multimodal knowledge from MLLMs.<n>Our results show multimodal attacks outperform text- or image-only ones, and that the most effective defense removes answer information from internal model states.
  arXiv  Detail & Related papers  (2025-05-01T01:54:00Z)
• MIRAGE: Multimodal Immersive Reasoning and Guided Exploration for Red-Team Jailbreak Attacks [85.3303135160762]
  MIRAGE is a novel framework that exploits narrative-driven context and role immersion to circumvent safety mechanisms in Multimodal Large Language Models.<n>It achieves state-of-the-art performance, improving attack success rates by up to 17.5% over the best baselines.<n>We demonstrate that role immersion and structured semantic reconstruction can activate inherent model biases, facilitating the model's spontaneous violation of ethical safeguards.
  arXiv  Detail & Related papers  (2025-03-24T20:38:42Z)
• Survey of Adversarial Robustness in Multimodal Large Language Models [17.926240920647892]
  Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance in artificial intelligence.<n>Their deployment in real-world applications raises significant concerns about adversarial vulnerabilities.<n>This paper reviews the adversarial robustness of MLLMs, covering different modalities.
  arXiv  Detail & Related papers  (2025-03-18T06:54:59Z)

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.