Manipulating Multimodal Agents via Cross-Modal Prompt Injection

• URL: http://arxiv.org/abs/2504.14348v3
• Date: Fri, 25 Apr 2025 09:46:35 GMT
• Title: Manipulating Multimodal Agents via Cross-Modal Prompt Injection
• Authors: Le Wang, Zonghao Ying, Tianyuan Zhang, Siyuan Liang, Shengshan Hu, Mingchuan Zhang, Aishan Liu, Xianglong Liu,
• Abstract summary: We identify a critical yet previously overlooked security vulnerability in multimodal agents.<n>We propose CrossInject, a novel attack framework in which attackers embed adversarial perturbations across multiple modalities.<n>Our method outperforms existing injection attacks, achieving at least a +26.4% increase in attack success rates.
• Score: 34.35145839873915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of multimodal large language models has redefined the agent paradigm by integrating language and vision modalities with external data sources, enabling agents to better interpret human instructions and execute increasingly complex tasks. However, in this work, we identify a critical yet previously overlooked security vulnerability in multimodal agents: cross-modal prompt injection attacks. To exploit this vulnerability, we propose CrossInject, a novel attack framework in which attackers embed adversarial perturbations across multiple modalities to align with target malicious content, allowing external instructions to hijack the agent's decision-making process and execute unauthorized tasks. Our approach consists of two key components. First, we introduce Visual Latent Alignment, where we optimize adversarial features to the malicious instructions in the visual embedding space based on a text-to-image generative model, ensuring that adversarial images subtly encode cues for malicious task execution. Subsequently, we present Textual Guidance Enhancement, where a large language model is leveraged to infer the black-box defensive system prompt through adversarial meta prompting and generate an malicious textual command that steers the agent's output toward better compliance with attackers' requests. Extensive experiments demonstrate that our method outperforms existing injection attacks, achieving at least a +26.4% increase in attack success rates across diverse tasks. Furthermore, we validate our attack's effectiveness in real-world multimodal autonomous agents, highlighting its potential implications for safety-critical applications.

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