Pushing the Frontier of Black-Box LVLM Attacks via Fine-Grained Detail Targeting

Black-box adversarial attacks on Large Vision-Language Models (LVLMs) are challenging due to missing gradients and complex multimodal boundaries. Prior state-of-the-art transfer-based approaches such as M-Attack match local crops between source and target images, but this induces high-variance, nearly orthogonal gradients across iterations. We attribute this instability to (i) ViT translation sensitivity, which yields spike-like gradients, and (ii) a structural asymmetry between source and target crops. We reformulate local matching as an asymmetric expectation over source transformations and target semantics, and introduce M-Attack-V2 based on this formulation. Multi-Crop Alignment (MCA) averages gradients from multiple local views per iteration, while Auxiliary Target Alignment (ATA) replaces aggressive target augmentation with a small set of semantically correlated auxiliary images. Patch Momentum replays historical crop gradients, and a patch-diverse surrogate ensemble (PE+) strengthens transferable directions. M-Attack-V2 raises attack success rates over M-Attack from 40% to 61% on GPT-6.1 Sol, from 40% to 60% on GPT-6 Astra, and from 85% to 97% on Claude Sonnet 5.5. Code and data are publicly available at: https://github.com/VILA-Lab/M-Attack-V2.