Related papers: Implicit Jailbreak Attacks via Cross-Modal Information Concealment on Vision-Language Models

Implicit Jailbreak Attacks via Cross-Modal Information Concealment on Vision-Language Models

URL: http://arxiv.org/abs/2505.16446v1
Date: Thu, 22 May 2025 09:34:47 GMT
Title: Implicit Jailbreak Attacks via Cross-Modal Information Concealment on Vision-Language Models
Authors: Zhaoxin Wang, Handing Wang, Cong Tian, Yaochu Jin,
Abstract summary: Previous jailbreak attacks often inject malicious instructions from text into less aligned modalities, such as vision.<n>We propose a novel implicit jailbreak framework termed IJA that stealthily embeds malicious instructions into images via at least significant bit steganography.<n>On commercial models like GPT-4o and Gemini-1.5 Pro, our method achieves attack success rates of over 90% using an average of only 3 queries.
Score: 20.99874786089634
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Multimodal large language models (MLLMs) enable powerful cross-modal reasoning capabilities. However, the expanded input space introduces new attack surfaces. Previous jailbreak attacks often inject malicious instructions from text into less aligned modalities, such as vision. As MLLMs increasingly incorporate cross-modal consistency and alignment mechanisms, such explicit attacks become easier to detect and block. In this work, we propose a novel implicit jailbreak framework termed IJA that stealthily embeds malicious instructions into images via least significant bit steganography and couples them with seemingly benign, image-related textual prompts. To further enhance attack effectiveness across diverse MLLMs, we incorporate adversarial suffixes generated by a surrogate model and introduce a template optimization module that iteratively refines both the prompt and embedding based on model feedback. On commercial models like GPT-4o and Gemini-1.5 Pro, our method achieves attack success rates of over 90% using an average of only 3 queries.

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