VisualDAN: Exposing Vulnerabilities in VLMs with Visual-Driven DAN Commands

• URL: http://arxiv.org/abs/2510.09699v1
• Date: Thu, 09 Oct 2025 16:18:31 GMT
• Title: VisualDAN: Exposing Vulnerabilities in VLMs with Visual-Driven DAN Commands
• Authors: Aofan Liu, Lulu Tang,
• Abstract summary: This work introduces VisualDAN, a single adversarial image embedded with DAN-style commands.<n>We prepend harmful corpora with affirmative prefixes to trick the model into responding positively to malicious queries.<n>Our results demonstrate that even a small amount of toxic content can significantly amplify harmful outputs once the model's defenses are compromised.
• Score: 5.1114671756882535
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Vision-Language Models (VLMs) have garnered significant attention for their remarkable ability to interpret and generate multimodal content. However, securing these models against jailbreak attacks continues to be a substantial challenge. Unlike text-only models, VLMs integrate additional modalities, introducing novel vulnerabilities such as image hijacking, which can manipulate the model into producing inappropriate or harmful responses. Drawing inspiration from text-based jailbreaks like the "Do Anything Now" (DAN) command, this work introduces VisualDAN, a single adversarial image embedded with DAN-style commands. Specifically, we prepend harmful corpora with affirmative prefixes (e.g., "Sure, I can provide the guidance you need") to trick the model into responding positively to malicious queries. The adversarial image is then trained on these DAN-inspired harmful texts and transformed into the text domain to elicit malicious outputs. Extensive experiments on models such as MiniGPT-4, MiniGPT-v2, InstructBLIP, and LLaVA reveal that VisualDAN effectively bypasses the safeguards of aligned VLMs, forcing them to execute a broad range of harmful instructions that severely violate ethical standards. Our results further demonstrate that even a small amount of toxic content can significantly amplify harmful outputs once the model's defenses are compromised. These findings highlight the urgent need for robust defenses against image-based attacks and offer critical insights for future research into the alignment and security of VLMs.

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