@inproceedings{kong-etal-2026-oscr,
    title = "{OSCR}-Attack: One-Shot Character Level Attacks through Self-Optimizing Continuous Relaxation",
    author = "Kong, Lingyi  and
      Liu, Zhuo  and
      Hu, Zhanghao  and
      Qiu, Qilong  and
      Yang, Yutao  and
      Xue, Jingjing  and
      Wang, Zheng  and
      Gui, Lin  and
      Nie, Feiping",
    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.1235/",
    pages = "24668--24683",
    ISBN = "979-8-89176-395-1",
    abstract = "Adversarial attacks have attracted growing attention across domains, including natural language processing (NLP). Character-level adversarial attacks preserve semantics, but they have received less attention because the discrete operations they use are costly and inefficient. Challenging these beliefs, we introduce two adaptively learnable matrices that transform discrete choices into continuous representations, enabling automatic one-shot multi-position, multi-character insertion. To optimize the two learnable matrices, we propose OSCR-Attack, an end-to-end framework based on gradient-based optimization, with a conflict resolution strategy that maps the optimized continuous distributions back into discrete insertion operations. Extensive experiments on three benchmarks with three open-source large language models (LLMs) show that OSCR-Attack improves attack success rate (ASR) by up to 21.45{\%} points and accelerates the attack by up to 3.66 times compared to recent baselines."
}