Attention Hijacking: Response Manipulation Across Queries in Vision-Language Models
Existing adversarial attacks on vision-language models (VLMs) can steer model outputs toward attacker-specified target responses, but their effectiveness often degrades when the same perturbed input is paired with different textual queries. This paper studies cross-query response manipulation, where a single adversarial example is expected to remain effective across diverse user queries. We first analyze the limitations of existing attacks and find that successful transfer is closely associated with preserving an image-dominant attention pattern during response generation. Motivated by the observation, we propose \textbf{Attention Hijacking}, a novel adversarial attack that explicitly ste...
| Authors | Zhiqiang Wang, Dongrui Liu, Yan Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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