Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs

ArXi:2605.21541v1 Announce Type: cross Multimodal large language models (MLLMs) remain vulnerable to transfer-based targeted attacks, where perturbations optimized on open-source surrogate encoders can generalize to closed-source MLLMs. A key challenge for improving adversarial transferability is to effectively capture the intrinsic visual focus shared across different models, such that perturbations align with transferable semantic cues rather than surrogate-specific behaviors.