@inproceedings{yang-etal-2026-cee,
title = "{CEE}: An Inference-Time Jailbreak Defense for Embodied Intelligence via Subspace Concept Rotation",
author = "Yang, Jirui and
Lin, Zheyu and
Lu, Zhihui and
Wang, Yinggui and
Wang, Lei and
Wei, Tao and
Duan, Qiang and
Du, Xin and
Yang, Shuhan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.758/",
pages = "15965--15996",
ISBN = "979-8-89176-462-0",
abstract = "Large language models (LLMs) are widely used for task understanding and action planning in embodied intelligence (EI) systems, but their adoption substantially increases vulnerability to jailbreak attacks. While recent work explores inference-time defenses, existing methods rely on $\textbf{static}$ interventions on intermediate representations, which often degrade generation quality and impair adherence to task instructions, reducing system usability in EI settings. We propose a $\textbf{dynamic}$ defense framework. For each EI inference request, we dynamically construct a task-specific safety-semantic subspace, project its hidden state to the most relevant direction, and apply SLERP rotation for adaptive safety control. At comparable defense success rates, our method preserves generation quality, improves usability, reduces tuning cost, and strengthens robustness in EI scenarios."
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<abstract>Large language models (LLMs) are widely used for task understanding and action planning in embodied intelligence (EI) systems, but their adoption substantially increases vulnerability to jailbreak attacks. While recent work explores inference-time defenses, existing methods rely on static interventions on intermediate representations, which often degrade generation quality and impair adherence to task instructions, reducing system usability in EI settings. We propose a dynamic defense framework. For each EI inference request, we dynamically construct a task-specific safety-semantic subspace, project its hidden state to the most relevant direction, and apply SLERP rotation for adaptive safety control. At comparable defense success rates, our method preserves generation quality, improves usability, reduces tuning cost, and strengthens robustness in EI scenarios.</abstract>
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%0 Conference Proceedings
%T CEE: An Inference-Time Jailbreak Defense for Embodied Intelligence via Subspace Concept Rotation
%A Yang, Jirui
%A Lin, Zheyu
%A Lu, Zhihui
%A Wang, Yinggui
%A Wang, Lei
%A Wei, Tao
%A Duan, Qiang
%A Du, Xin
%A Yang, Shuhan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yang-etal-2026-cee
%X Large language models (LLMs) are widely used for task understanding and action planning in embodied intelligence (EI) systems, but their adoption substantially increases vulnerability to jailbreak attacks. While recent work explores inference-time defenses, existing methods rely on static interventions on intermediate representations, which often degrade generation quality and impair adherence to task instructions, reducing system usability in EI settings. We propose a dynamic defense framework. For each EI inference request, we dynamically construct a task-specific safety-semantic subspace, project its hidden state to the most relevant direction, and apply SLERP rotation for adaptive safety control. At comparable defense success rates, our method preserves generation quality, improves usability, reduces tuning cost, and strengthens robustness in EI scenarios.
%U https://aclanthology.org/2026.findings-emnlp.758/
%P 15965-15996
Markdown (Informal)
[CEE: An Inference-Time Jailbreak Defense for Embodied Intelligence via Subspace Concept Rotation](https://aclanthology.org/2026.findings-emnlp.758/) (Yang et al., Findings 2026)
ACL
- Jirui Yang, Zheyu Lin, Zhihui Lu, Yinggui Wang, Lei Wang, Tao Wei, Qiang Duan, Xin Du, and Shuhan Yang. 2026. CEE: An Inference-Time Jailbreak Defense for Embodied Intelligence via Subspace Concept Rotation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 15965–15996, Budapest, Hungary. Association for Computational Linguistics.