Text is All You Need for Vision-Language Model Jailbreaking

Large Vision-Language Models (LVLMs) are increasingly equipped with robust safety safeguards to prevent responses to harmful or disallowed prompts. However, these defenses often focus on analyzing explicit textual inputs or relevant visual scenes. In this work, we introduce Text-DJ, a novel jailbreak attack that bypasses these safeguards by exploiting the model's Optical Character Recognition (OCR) capability. Our methodology consists of three stages. First, we decompose a single harmful query into multiple and semantically related but more benign sub-queries. Second, we pick a set of distraction queries that are maximally irrelevant to the harmful query. Third, we present all decomposed sub-queries and distraction queries to the LVLM simultaneously as a grid of images, with the position of the sub-queries being middle within the grid. We demonstrate that this method successfully circumvents the safety alignment of state-of-the-art LVLMs. We argue this attack succeeds by (1) converting text-based prompts into images, bypassing standard text-based filters, and (2) inducing distractions, where the model's safety protocols fail to link the scattered sub-queries within a high number of irrelevant queries. Overall, our findings expose a critical vulnerability in LVLMs' OCR capabilities that are not robust to dispersed, multi-image adversarial inputs, highlighting the need for defenses for fragmented multimodal inputs.

Key Contributions

Text-DJ: a black-box, model-agnostic jailbreak that decomposes harmful queries into benign sub-queries and embeds them in a distractor image grid, exploiting LVLM OCR to bypass text-based safety filters
Demonstrates that safety alignment fails when harmful prompts are scattered across multi-image OCR inputs alongside irrelevant distraction queries
Ablation studies showing semantic distraction is more effective than visual distraction, pinpointing OCR safety as a fundamental unaddressed vulnerability