A Systematic Study of Cross-Modal Typographic Attacks on Audio-Visual Reasoning
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a systematic study of cross-modal typographic attacks on audio-visual reasoning, revealing significant vulnerabilities in multi-modal language models. The comprehensive analysis of attack strategies and their effectiveness contributes valuable insights into the security of MLLMs, highlighting an underexplored area in machine learning research.
Methodology
The methodology employed in this paper is robust, focusing on a systematic study of cross-modal typographic attacks on audio-visual reasoning. The authors effectively design experiments that illustrate the interactions between different modalities (audio, visual, and text) and how they can be exploited through coordinated attacks. The approach is well-structured, with clear definitions of the attack vectors and metrics for evaluating success rates. However, the paper could benefit from a more detailed explanation of the algorithms used for generating these attacks and the specific architectures of the MLLMs tested.
Results & evaluation
The experiments are comprehensive, covering multiple frontier MLLMs and various tasks, which adds to the credibility of the findings. The reported attack success rates provide compelling evidence of the effectiveness of multi-modal typography as an attack strategy. However, the paper lacks a detailed comparison with existing unimodal attack strategies, which would strengthen the argument for the necessity of this research. The benchmarks used are relevant, but the inclusion of more diverse datasets could enhance the robustness of the results.
Reproducibility
The paper mentions that code and data will be publicly available, which is a positive aspect for reproducibility. However, without specific URLs or links provided in the paper, it is difficult to assess the ease of access to these resources. Detailed descriptions of the experimental setups and configurations would also aid in reproducibility.
Limitations
One limitation is the focus on specific MLLMs without exploring the generalizability of the findings across other models or architectures. Additionally, the paper does not address potential countermeasures against these attacks, which could provide a more balanced view of the implications of their findings.
Broader impact
The implications of this research are significant, especially in safety-critical applications where audio-visual MLLMs are deployed. Understanding vulnerabilities in these systems is crucial for developing more robust models. The findings could inform future research on security measures and resilience in multi-modal AI systems. The exploration of cross-modal attacks opens new avenues for further investigation into the robustness of AI systems. This paper presents a systematic study of cross-modal typographic attacks on audio-visual reasoning, revealing significant vulnerabilities in multi-modal language models. The comprehensive analysis of attack strategies and their effectiveness contributes valuable insights into the security of MLLMs, highlighting an underexplored area in machine learning research.
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