@inproceedings{sun-etal-2026-infinite,
    title = "Infinite Babble: Inflating 3{D} Vision-Language Model Inference Overhead via Adversarial Geometric Perturbation",
    author = "Sun, Shuoyang  and
      Hong, Jiaxin  and
      Zhang, Yv  and
      Gao, Kuofeng  and
      Fang, Hao  and
      Mo, Fan  and
      Chen, Bin  and
      Xia, Shu-Tao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.259/",
    pages = "5249--5267",
    ISBN = "979-8-89176-395-1",
    abstract = "3D Vision-Language Models (3D-VLMs) have emerged as the critical cognitive backbone for spatial intelligence, enabling precise reasoning over unstructured 3D data. While these models serve as the foundation for downstream robotics and embodied systems, their reliance on autoregressive decoding introduces a fundamental vulnerability regarding inference efficiency. In this work, we present $\textbf{Inflate3D}$, a novel adversarial framework designed to trigger computational and economic exhaustion in 3D-VLMs. Specifically, we exploit the model{'}s sensitivity to untrusted 3D assets to hijack the generation process. Inflate3D operates by injecting imperceptible noise that forces the model into a state of pathological verbosity, effectively stalling the inference pipeline. Our approach comprises two synergistic strategies: (1) a $\textit{semantic-aware adversarial manipulation}$ that leverages internal representations to selectively perturb semantically critical regions while preserving geometric structure, and (2) a $\textit{trajectory disruption mechanism}$ that manipulates token probabilities to suppress End-of-Sequence (EOS) emission, thereby prolonging decoding and inducing verbose outputs. Experiments on standard benchmarks show that Inflate3D amplifies output length and energy consumption by up to $\textbf{6.45$\times$}$, demonstrating a potent capability to drain system resources. These findings expose a critical blind spot in multimodal alignment, highlighting the urgent need to secure spatial foundation models against resource exhaustion attacks."
}