Title: Why does Weak-OOD Help? A Further Step Towards Understanding Jailbreaking VLMs
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Jailbreak Attack, VLM

Abstract: Large Vision-Language Models (VLMs) are susceptible to jailbreak attacks: re-
searchers have developed a variety of attack strategies that can successfully by-
pass the safety mechanisms of VLMs. Among these approaches, jailbreak meth-
ods based on the Out-of-Distribution (OOD) strategy have garnered widespread
attention due to their simplicity and effectiveness. This paper further advances
the in-depth understanding of OOD-based VLM jailbreak methods. Experimen-
tal results demonstrate that jailbreak samples generated via mild OOD strategies
exhibit superior performance in circumventing the safety constraints of VLMs—a
phenomenon we define as “weak-OOD”. To unravel the underlying causes of this
phenomenon, this study takes SI-Attack, a typical OOD-based jailbreak method,
as the research object. We attribute this phenomenon to a trade-off between two
dominant factors: input intent perception and model refusal triggering. The incon-
sistency in how these two factors respond to OOD manipulations gives rise to this
phenomenon. Furthermore, we provide a theoretical argument for the inevitabil-
ity of such inconsistency from the perspective of discrepancies between model
pre-training and alignment processes. Building on the above insights, we draw
inspiration from optical character recognition (OCR) capability enhancement—a
core task in the pre-training phase of mainstream VLMs. Leveraging this ca-
pability, we design a simple yet highly effective VLM jailbreak method, whose
performance outperforms that of SOTA baselines. Code is available at Github

TL;DR: VLMs are vulnerable to jailbreaks (OOD methods effective). This study finds "weak-OOD" boosts jailbreak performance (linked to input intent-model refusal trade-off from training-alignment gaps) and designs a new method outperforming baselines.