@inproceedings{chen-etal-2025-llm-jailbreak,
title = "{LLM} Jailbreak Detection for (Almost) Free!",
author = "Chen, Guorui and
Xia, Yifan and
Jia, Xiaojun and
Li, Zhijiang and
Torr, Philip and
Gu, Jindong",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.309/",
doi = "10.18653/v1/2025.findings-emnlp.309",
pages = "5777--5807",
ISBN = "979-8-89176-335-7",
abstract = "Large language models (LLMs) enhance security through alignment when widely used, but remain susceptible to jailbreak attacks capable of producing inappropriate content. Jailbreak detection methods show promise in mitigating jailbreak attacks through the assistance of other models or multiple model inferences. However, existing methods entail significant computational costs. In this paper, we first present a finding that the difference in output distributions between jailbreak and benign prompts can be employed for detecting jailbreak prompts. Based on this finding, we propose a Free Jailbreak Detection (FJD) which prepends an affirmative instruction to the input and scales the logits by temperature to distinguish between jailbreak and benign prompts through the confidence of the first token. Furthermore, we enhance the detection performance of FJD through the integration of virtual instruction learning. Extensive experiments on aligned LLMs show that our FJD can effectively detect jailbreak prompts with almost no additional computational costs during LLM inference."
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<abstract>Large language models (LLMs) enhance security through alignment when widely used, but remain susceptible to jailbreak attacks capable of producing inappropriate content. Jailbreak detection methods show promise in mitigating jailbreak attacks through the assistance of other models or multiple model inferences. However, existing methods entail significant computational costs. In this paper, we first present a finding that the difference in output distributions between jailbreak and benign prompts can be employed for detecting jailbreak prompts. Based on this finding, we propose a Free Jailbreak Detection (FJD) which prepends an affirmative instruction to the input and scales the logits by temperature to distinguish between jailbreak and benign prompts through the confidence of the first token. Furthermore, we enhance the detection performance of FJD through the integration of virtual instruction learning. Extensive experiments on aligned LLMs show that our FJD can effectively detect jailbreak prompts with almost no additional computational costs during LLM inference.</abstract>
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%0 Conference Proceedings
%T LLM Jailbreak Detection for (Almost) Free!
%A Chen, Guorui
%A Xia, Yifan
%A Jia, Xiaojun
%A Li, Zhijiang
%A Torr, Philip
%A Gu, Jindong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F chen-etal-2025-llm-jailbreak
%X Large language models (LLMs) enhance security through alignment when widely used, but remain susceptible to jailbreak attacks capable of producing inappropriate content. Jailbreak detection methods show promise in mitigating jailbreak attacks through the assistance of other models or multiple model inferences. However, existing methods entail significant computational costs. In this paper, we first present a finding that the difference in output distributions between jailbreak and benign prompts can be employed for detecting jailbreak prompts. Based on this finding, we propose a Free Jailbreak Detection (FJD) which prepends an affirmative instruction to the input and scales the logits by temperature to distinguish between jailbreak and benign prompts through the confidence of the first token. Furthermore, we enhance the detection performance of FJD through the integration of virtual instruction learning. Extensive experiments on aligned LLMs show that our FJD can effectively detect jailbreak prompts with almost no additional computational costs during LLM inference.
%R 10.18653/v1/2025.findings-emnlp.309
%U https://aclanthology.org/2025.findings-emnlp.309/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.309
%P 5777-5807
Markdown (Informal)
[LLM Jailbreak Detection for (Almost) Free!](https://aclanthology.org/2025.findings-emnlp.309/) (Chen et al., Findings 2025)
ACL
- Guorui Chen, Yifan Xia, Xiaojun Jia, Zhijiang Li, Philip Torr, and Jindong Gu. 2025. LLM Jailbreak Detection for (Almost) Free!. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5777–5807, Suzhou, China. Association for Computational Linguistics.