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    <titleInfo>
        <title>Distract Large Language Models for Automatic Jailbreak Attack</title>
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    <name type="personal">
        <namePart type="given">Zeguan</namePart>
        <namePart type="family">Xiao</namePart>
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        <namePart type="family">Yang</namePart>
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    <name type="personal">
        <namePart type="given">Guanhua</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Yun</namePart>
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            <title>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
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        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Extensive efforts have been made before the public release of Large language models (LLMs) to align their behaviors with human values. However, even meticulously aligned LLMs remain vulnerable to malicious manipulations such as jailbreaking, leading to unintended behaviors. In this work, we propose a novel black-box jailbreak framework for automated red teaming of LLMs. We designed malicious content concealing and memory reframing with an iterative optimization algorithm to jailbreak LLMs, motivated by the research about the distractibility and over-confidence phenomenon of LLMs. Extensive experiments of jailbreaking both open-source and proprietary LLMs demonstrate the superiority of our framework in terms of effectiveness, scalability and transferability. We also evaluate the effectiveness of existing jailbreak defense methods against our attack and highlight the crucial need to develop more effective and practical defense strategies.</abstract>
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        <url>https://aclanthology.org/2024.emnlp-main.908</url>
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        <date>2024-11</date>
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            <start>16230</start>
            <end>16244</end>
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%0 Conference Proceedings
%T Distract Large Language Models for Automatic Jailbreak Attack
%A Xiao, Zeguan
%A Yang, Yan
%A Chen, Guanhua
%A Chen, Yun
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F xiao-etal-2024-distract
%X Extensive efforts have been made before the public release of Large language models (LLMs) to align their behaviors with human values. However, even meticulously aligned LLMs remain vulnerable to malicious manipulations such as jailbreaking, leading to unintended behaviors. In this work, we propose a novel black-box jailbreak framework for automated red teaming of LLMs. We designed malicious content concealing and memory reframing with an iterative optimization algorithm to jailbreak LLMs, motivated by the research about the distractibility and over-confidence phenomenon of LLMs. Extensive experiments of jailbreaking both open-source and proprietary LLMs demonstrate the superiority of our framework in terms of effectiveness, scalability and transferability. We also evaluate the effectiveness of existing jailbreak defense methods against our attack and highlight the crucial need to develop more effective and practical defense strategies.
%U https://aclanthology.org/2024.emnlp-main.908
%P 16230-16244

Markdown (Informal)

[Distract Large Language Models for Automatic Jailbreak Attack](https://aclanthology.org/2024.emnlp-main.908) (Xiao et al., EMNLP 2024)

• Distract Large Language Models for Automatic Jailbreak Attack (Xiao et al., EMNLP 2024)

ACL

• Zeguan Xiao, Yan Yang, Guanhua Chen, and Yun Chen. 2024. Distract Large Language Models for Automatic Jailbreak Attack. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 16230–16244, Miami, Florida, USA. Association for Computational Linguistics.