@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T OSCR-Attack: One-Shot Character Level Attacks through Self-Optimizing Continuous Relaxation
%A Kong, Lingyi
%A Liu, Zhuo
%A Hu, Zhanghao
%A Qiu, Qilong
%A Yang, Yutao
%A Xue, Jingjing
%A Wang, Zheng
%A Gui, Lin
%A Nie, Feiping
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F kong-etal-2026-oscr
%X 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.
%U https://aclanthology.org/2026.findings-acl.1235/
%P 24668-24683
Markdown (Informal)
[OSCR-Attack: One-Shot Character Level Attacks through Self-Optimizing Continuous Relaxation](https://aclanthology.org/2026.findings-acl.1235/) (Kong et al., Findings 2026)
ACL
- Lingyi Kong, Zhuo Liu, Zhanghao Hu, Qilong Qiu, Yutao Yang, Jingjing Xue, Zheng Wang, Lin Gui, and Feiping Nie. 2026. OSCR-Attack: One-Shot Character Level Attacks through Self-Optimizing Continuous Relaxation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 24668–24683, San Diego, California, United States. Association for Computational Linguistics.