@inproceedings{xiang-etal-2026-rshield,
title = "{RS}hield: A User-level Traceable Backdoor Watermark for {LLM}s in Embedding-as-a-Service",
author = "Xiang, Lingyun and
Zhong, Yufan and
Ou, Chengfu and
Xia, Zhihua and
Yang, Chunfang and
Zeng, Daojian and
Fu, Zhangjie",
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.1347/",
pages = "27014--27028",
ISBN = "979-8-89176-395-1",
abstract = "Embedding-as-a-Service (EaaS) has emerged as a critical paradigm for commercializing large language models (LLMs). However, existing backdoor watermarking techniques are fundamentally limited to ``zero-bit'' detection, which prevents user-level traceability in multi-user EaaS scenarios. To address these limitations, we propose RShield, a multi-bit backdoor watermarking that enables reliable user-level attribution of LLMs for EaaS under model extraction attacks. RShield integrates Reed-Solomon error-correcting codes with orthogonal feature mapping to introduce highly-structured redundancy, constructing fault-tolerant symbol sequences for multi-bit watermark space, thereby staying recoverable even after aggressive extraction noise condition.To mitigate semantic distortion under the interference of noise channel, RShield employs a lightweight Adapter to adaptively inject multi-bit watermarks in the feature space, preserving the quality of EaaS while achieving a user-level traceability.Extensive experiments on four NLP benchmarks demonstrate that RShield efficiently achieves 100{\%} multi-bit watermark recovery and high semantic fidelity under model extraction attacks compared to existing methods, while significantly reducing the degradation of watermarking on downstream task performance."
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<abstract>Embedding-as-a-Service (EaaS) has emerged as a critical paradigm for commercializing large language models (LLMs). However, existing backdoor watermarking techniques are fundamentally limited to “zero-bit” detection, which prevents user-level traceability in multi-user EaaS scenarios. To address these limitations, we propose RShield, a multi-bit backdoor watermarking that enables reliable user-level attribution of LLMs for EaaS under model extraction attacks. RShield integrates Reed-Solomon error-correcting codes with orthogonal feature mapping to introduce highly-structured redundancy, constructing fault-tolerant symbol sequences for multi-bit watermark space, thereby staying recoverable even after aggressive extraction noise condition.To mitigate semantic distortion under the interference of noise channel, RShield employs a lightweight Adapter to adaptively inject multi-bit watermarks in the feature space, preserving the quality of EaaS while achieving a user-level traceability.Extensive experiments on four NLP benchmarks demonstrate that RShield efficiently achieves 100% multi-bit watermark recovery and high semantic fidelity under model extraction attacks compared to existing methods, while significantly reducing the degradation of watermarking on downstream task performance.</abstract>
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%0 Conference Proceedings
%T RShield: A User-level Traceable Backdoor Watermark for LLMs in Embedding-as-a-Service
%A Xiang, Lingyun
%A Zhong, Yufan
%A Ou, Chengfu
%A Xia, Zhihua
%A Yang, Chunfang
%A Zeng, Daojian
%A Fu, Zhangjie
%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 xiang-etal-2026-rshield
%X Embedding-as-a-Service (EaaS) has emerged as a critical paradigm for commercializing large language models (LLMs). However, existing backdoor watermarking techniques are fundamentally limited to “zero-bit” detection, which prevents user-level traceability in multi-user EaaS scenarios. To address these limitations, we propose RShield, a multi-bit backdoor watermarking that enables reliable user-level attribution of LLMs for EaaS under model extraction attacks. RShield integrates Reed-Solomon error-correcting codes with orthogonal feature mapping to introduce highly-structured redundancy, constructing fault-tolerant symbol sequences for multi-bit watermark space, thereby staying recoverable even after aggressive extraction noise condition.To mitigate semantic distortion under the interference of noise channel, RShield employs a lightweight Adapter to adaptively inject multi-bit watermarks in the feature space, preserving the quality of EaaS while achieving a user-level traceability.Extensive experiments on four NLP benchmarks demonstrate that RShield efficiently achieves 100% multi-bit watermark recovery and high semantic fidelity under model extraction attacks compared to existing methods, while significantly reducing the degradation of watermarking on downstream task performance.
%U https://aclanthology.org/2026.findings-acl.1347/
%P 27014-27028
Markdown (Informal)
[RShield: A User-level Traceable Backdoor Watermark for LLMs in Embedding-as-a-Service](https://aclanthology.org/2026.findings-acl.1347/) (Xiang et al., Findings 2026)
ACL
- Lingyun Xiang, Yufan Zhong, Chengfu Ou, Zhihua Xia, Chunfang Yang, Daojian Zeng, and Zhangjie Fu. 2026. RShield: A User-level Traceable Backdoor Watermark for LLMs in Embedding-as-a-Service. In Findings of the Association for Computational Linguistics: ACL 2026, pages 27014–27028, San Diego, California, United States. Association for Computational Linguistics.