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arXiv 2408.08464cs.CR
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MMJ-Bench:多模态大语言模型越狱攻击与防御的综合研究

$\textit{MMJ-Bench}$: A Comprehensive Study on Jailbreak Attacks and Defenses for Multimodal Large Language Models

Fenghua Weng, Yue Xu, Chengyan Fu, Wenjie Wang

AI总结:

本文提出MMJ-Bench统一评估框架,系统比较针对多模态大语言模型的越狱攻击与防御方法,并提供首个公开基准,揭示未来研究方向。

摘要:

随着深度学习的进步,大语言模型(LLMs)及其多模态对应物——多模态大语言模型(MLLMs)——在许多现实世界任务中展现出卓越的性能。然而,MLLMs面临着重大的安全挑战,例如越狱攻击,攻击者试图绕过模型的安全对齐机制以诱使其产生有害响应。MLLMs面临的越狱攻击威胁既源于LLMs固有的脆弱性,也源于MLLMs所处理的多个信息通道。尽管已经提出了各种攻击和防御方法,但在统一和全面的评估方面仍存在显著空白,因为每种方法都在不同的数据集和指标上进行评估,使得无法比较各方法的有效性。为了填补这一空白,我们提出了MMJ-Bench,一个用于评估MLLMs越狱攻击和防御技术的统一流水线。通过大量实验,我们评估了各种攻击方法针对最先进(SoTA)MLLMs的有效性,并评估了防御机制对防御效果和正常任务模型效用的影响。我们的综合评估通过提供一个统一且系统的评估框架以及首个公开可用的MLLM越狱研究基准,为该领域做出了贡献。我们还展示了一些具有洞察力的发现,为未来研究指明了方向。

英文摘要:

As deep learning advances, Large Language Models (LLMs) and their multimodal counterparts, Multimodal Large Language Models (MLLMs), have shown exceptional performance in many real-world tasks. However, MLLMs face significant security challenges, such as jailbreak attacks, where attackers attempt to bypass the model's safety alignment to elicit harmful responses. The threat of jailbreak attacks on MLLMs arises from both the inherent vulnerabilities of LLMs and the multiple information channels that MLLMs process. While various attacks and defenses have been proposed, there is a notable gap in unified and comprehensive evaluations, as each method is evaluated on different dataset and metrics, making it impossible to compare the effectiveness of each method. To address this gap, we introduce \textit{MMJ-Bench}, a unified pipeline for evaluating jailbreak attacks and defense techniques for MLLMs. Through extensive experiments, we assess the effectiveness of various attack methods against SoTA MLLMs and evaluate the impact of defense mechanisms on both defense effectiveness and model utility for normal tasks. Our comprehensive evaluation contribute to the field by offering a unified and systematic evaluation framework and the first public-available benchmark for MLLM jailbreak research. We also demonstrate several insightful findings that highlights directions for future studies.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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