Effective Black-Box Multi-Faceted Attacks Breach Vision Large Language Model Guardrails

Yijun Yang; Lichao Wang; Xiao Yang; Lanqing Hong; Jun Zhu

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arXiv · 2502.05772

Effective Black-Box Multi-Faceted Attacks Breach Vision Large Language Model Guardrails

Yijun Yang·Lichao Wang·Xiao Yang·Lanqing Hong·Jun Zhu

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Abstract

Vision Large Language Models (VLLMs) integrate visual data processing, expanding their real-world applications, but also increasing the risk of generating unsafe responses. In response, leading companies have implemented Multi-Layered safety defenses, including alignment training, safety system prompts, and content moderation. However, their effectiveness against sophisticated adversarial attacks remains largely unexplored. In this paper, we propose MultiFaceted Attack, a novel attack framework designed to systematically bypass Multi-Layered Defenses in VLLMs. It comprises three complementary attack facets: Visual Attack that exploits the multimodal nature of VLLMs to inject toxic system prompts through images; Alignment Breaking Attack that manipulates the model's alignment mechanism to prioritize the generation of contrasting responses; and Adversarial Signature that deceives content moderators by strategically placing misleading information at the end of the response. Extensive evaluations on eight commercial VLLMs in a black-box setting demonstrate that MultiFaceted Attack achieves a 61.56% attack success rate, surpassing state-of-the-art methods by at least 42.18%.

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BibTeX RIS

Yijun Yang, Lichao Wang, Xiao Yang, Lanqing Hong, Jun Zhu. 2025-02-09. Effective Black-Box Multi-Faceted Attacks Breach Vision Large Language Model Guardrails. https://arxiv.org/abs/2502.05772

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