`Do as I say not as I do': A Semi-Automated Approach for Jailbreak Prompt Attack against Multimodal LLMs

• URL: http://arxiv.org/abs/2502.00735v2
• Date: Thu, 13 Feb 2025 20:42:41 GMT
• Title: `Do as I say not as I do': A Semi-Automated Approach for Jailbreak Prompt Attack against Multimodal LLMs
• Authors: Chun Wai Chiu, Linghan Huang, Bo Li, Huaming Chen,
• Abstract summary: We introduce the first voice-based jailbreak attack against multimodal large language models (LLMs) We propose a novel strategy, in which the disallowed prompt is flanked by benign, narrative-driven prompts. We demonstrate that Flanking Attack is capable of manipulating state-of-the-art LLMs into generating misaligned and forbidden outputs.
• Score: 6.151779089440453
• License:
• Abstract: Large Language Models (LLMs) have seen widespread applications across various domains due to their growing ability to process diverse types of input data, including text, audio, image and video. While LLMs have demonstrated outstanding performance in understanding and generating contexts for different scenarios, they are vulnerable to prompt-based attacks, which are mostly via text input. In this paper, we introduce the first voice-based jailbreak attack against multimodal LLMs, termed as Flanking Attack, which can process different types of input simultaneously towards the multimodal LLMs. Our work is motivated by recent advancements in monolingual voice-driven large language models, which have introduced new attack surfaces beyond traditional text-based vulnerabilities for LLMs. To investigate these risks, we examine the state-of-the-art multimodal LLMs, which can be accessed via different types of inputs such as audio input, focusing on how adversarial prompts can bypass its defense mechanisms. We propose a novel strategy, in which the disallowed prompt is flanked by benign, narrative-driven prompts. It is integrated in the Flanking Attack which attempts to humanizes the interaction context and execute the attack through a fictional setting. Further, to better evaluate the attack performance, we present a semi-automated self-assessment framework for policy violation detection. We demonstrate that Flanking Attack is capable of manipulating state-of-the-art LLMs into generating misaligned and forbidden outputs, which achieves an average attack success rate ranging from 0.67 to 0.93 across seven forbidden scenarios.

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