@inproceedings{luo-etal-2025-dynamic,
title = "Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models",
author = "Luo, Weidi and
Cao, He and
Liu, Zijing and
Wang, Yu and
Wong, Aidan and
Feng, Bin and
Yao, Yuan and
Li, Yu",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.368/",
doi = "10.18653/v1/2025.findings-naacl.368",
pages = "6599--6620",
ISBN = "979-8-89176-195-7",
abstract = "With the extensive deployment of Large Language Models (LLMs), ensuring their safety has become increasingly critical. However, existing defense methods often struggle with two key issues: (i) inadequate defense capabilities, particularly in domain-specific scenarios like chemistry, where a lack of specialized knowledge can lead to the generation of harmful responses to malicious queries. (ii) over-defensiveness, which compromises the general utility and responsiveness of LLMs. To mitigate these issues, we introduce a multi-agents-based defense framework, Guide for Defense (G4D), which leverages accurate external information to provide an unbiased summary of user intentions and analytically grounded safety response guidance. Extensive experiments on popular jailbreak attacks and benign datasets show that our G4D can enhance LLM{'}s robustness against jailbreak attacks on general and domain-specific scenarios without compromising the model{'}s general functionality."
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<abstract>With the extensive deployment of Large Language Models (LLMs), ensuring their safety has become increasingly critical. However, existing defense methods often struggle with two key issues: (i) inadequate defense capabilities, particularly in domain-specific scenarios like chemistry, where a lack of specialized knowledge can lead to the generation of harmful responses to malicious queries. (ii) over-defensiveness, which compromises the general utility and responsiveness of LLMs. To mitigate these issues, we introduce a multi-agents-based defense framework, Guide for Defense (G4D), which leverages accurate external information to provide an unbiased summary of user intentions and analytically grounded safety response guidance. Extensive experiments on popular jailbreak attacks and benign datasets show that our G4D can enhance LLM’s robustness against jailbreak attacks on general and domain-specific scenarios without compromising the model’s general functionality.</abstract>
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%0 Conference Proceedings
%T Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models
%A Luo, Weidi
%A Cao, He
%A Liu, Zijing
%A Wang, Yu
%A Wong, Aidan
%A Feng, Bin
%A Yao, Yuan
%A Li, Yu
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F luo-etal-2025-dynamic
%X With the extensive deployment of Large Language Models (LLMs), ensuring their safety has become increasingly critical. However, existing defense methods often struggle with two key issues: (i) inadequate defense capabilities, particularly in domain-specific scenarios like chemistry, where a lack of specialized knowledge can lead to the generation of harmful responses to malicious queries. (ii) over-defensiveness, which compromises the general utility and responsiveness of LLMs. To mitigate these issues, we introduce a multi-agents-based defense framework, Guide for Defense (G4D), which leverages accurate external information to provide an unbiased summary of user intentions and analytically grounded safety response guidance. Extensive experiments on popular jailbreak attacks and benign datasets show that our G4D can enhance LLM’s robustness against jailbreak attacks on general and domain-specific scenarios without compromising the model’s general functionality.
%R 10.18653/v1/2025.findings-naacl.368
%U https://aclanthology.org/2025.findings-naacl.368/
%U https://doi.org/10.18653/v1/2025.findings-naacl.368
%P 6599-6620
Markdown (Informal)
[Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models](https://aclanthology.org/2025.findings-naacl.368/) (Luo et al., Findings 2025)
ACL
- Weidi Luo, He Cao, Zijing Liu, Yu Wang, Aidan Wong, Bin Feng, Yuan Yao, and Yu Li. 2025. Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 6599–6620, Albuquerque, New Mexico. Association for Computational Linguistics.