Re-Triggering Safeguards within LLMs for Jailbreak Detection
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916000881967104 |
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| author | Lin, Zheng Niu, Zhenxing Ji, Haoxuan Huang, Yuzhe Gao, Haichang |
| author_facet | Lin, Zheng Niu, Zhenxing Ji, Haoxuan Huang, Yuzhe Gao, Haichang |
| contents | This paper proposes a jailbreaking prompt detection method for large language models (LLMs) to defend against jailbreak attacks. Although recent LLMs are equipped with built-in safeguards, it remains possible to craft jailbreaking prompts that bypass them. We argue that such jailbreaking prompts are inherently fragile, and thus introduce an embedding disruption method to re-activate the safeguards within LLMs. Unlike previous defense methods that aim to serve as standalone solutions, our approach instead cooperates with the LLM's internal defense mechanisms by re-triggering them. Moreover, through extensive analysis, we gain a comprehensive understanding of the disruption effects and develop an efficient search algorithm to identify appropriate disruptions for effective jailbreak detection. Extensive experiments demonstrate that our approach effectively defends against state-of-the-art jailbreak attacks in white-box and black-box settings, and remains robust even against adaptive attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10611 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Re-Triggering Safeguards within LLMs for Jailbreak Detection Lin, Zheng Niu, Zhenxing Ji, Haoxuan Huang, Yuzhe Gao, Haichang Cryptography and Security Artificial Intelligence This paper proposes a jailbreaking prompt detection method for large language models (LLMs) to defend against jailbreak attacks. Although recent LLMs are equipped with built-in safeguards, it remains possible to craft jailbreaking prompts that bypass them. We argue that such jailbreaking prompts are inherently fragile, and thus introduce an embedding disruption method to re-activate the safeguards within LLMs. Unlike previous defense methods that aim to serve as standalone solutions, our approach instead cooperates with the LLM's internal defense mechanisms by re-triggering them. Moreover, through extensive analysis, we gain a comprehensive understanding of the disruption effects and develop an efficient search algorithm to identify appropriate disruptions for effective jailbreak detection. Extensive experiments demonstrate that our approach effectively defends against state-of-the-art jailbreak attacks in white-box and black-box settings, and remains robust even against adaptive attacks. |
| title | Re-Triggering Safeguards within LLMs for Jailbreak Detection |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2605.10611 |