FENCE：一个金融和多模态越狱检测数据集
FENCE: A Financial and Multimodal Jailbreak Detection Dataset
AI总结：
针对金融领域多模态越狱检测资源匮乏的问题，提出FENCE数据集，包含韩英双语文本和图像，用于训练和评估检测器，实验表明基线检测器准确率达99%。
摘要：
越狱对大型语言模型（LLM）和视觉语言模型（VLM）的部署构成重大风险。VLM尤其脆弱，因为它们处理文本和图像，创造了更广泛的攻击面。然而，可用于越狱检测的资源很少，特别是在金融领域。为填补这一空白，我们提出了FENCE，一个双语（韩语-英语）多模态数据集，用于训练和评估金融应用中的越狱检测器。FENCE通过金融相关查询与图像威胁配对，强调领域真实性。使用商业和开源VLM进行的实验揭示了持续的脆弱性，GPT-4o显示出可测量的攻击成功率，而开源模型则表现出更大的暴露。在FENCE上训练的基线检测器实现了99%的分布内准确率，并在外部基准测试中保持强劲性能，突显了该数据集在训练可靠检测模型方面的鲁棒性。FENCE为推进金融领域的多模态越狱检测以及支持敏感领域中更安全、更可靠的AI系统提供了重点资源。警告：本文包含可能具有冒犯性的示例数据。
英文摘要：
Jailbreaking poses a significant risk to the deployment of Large Language Models (LLMs) and Vision Language Models (VLMs). VLMs are particularly vulnerable because they process both text and images, creating broader attack surfaces. However, available resources for jailbreak detection are scarce, particularly in finance. To address this gap, we present FENCE, a bilingual (Korean-English) multimodal dataset for training and evaluating jailbreak detectors in financial applications. FENCE emphasizes domain realism through finance-relevant queries paired with image-grounded threats. Experiments with commercial and open-source VLMs reveal consistent vulnerabilities, with GPT-4o showing measurable attack success rates and open-source models displaying greater exposure. A baseline detector trained on FENCE achieves 99 percent in-distribution accuracy and maintains strong performance on external benchmarks, underscoring the dataset's robustness for training reliable detection models. FENCE provides a focused resource for advancing multimodal jailbreak detection in finance and for supporting safer, more reliable AI systems in sensitive domains. Warning: This paper includes example data that may be offensive.
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。
