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    <abstract>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 comprises 10k finance-domain text–image pairs across more than 15 finance categories, constructed via a three-step pipeline: transforming real-world financial FAQs into harmful queries using GPT-4o, collecting query-relevant images via keyword-based crawling, and fusing text and images with diverse layout strategies. Labels were assigned using GPT-4o as an evaluator, with human validation confirming 95% agreement. Experiments on 15 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% in-distribution accuracy and maintains strong performance on external benchmarks. FENCE provides a focused resource for advancing multimodal jailbreak detection in finance and supporting safer AI deployment in sensitive domains. Content Warning: This paper includes example data that may be offensive.</abstract>
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%0 Conference Proceedings
%T FENCE: A Financial and Multimodal Jailbreak Detection Dataset
%A Kim, Mirae
%A Jeong, Seonghun
%A Kwak, Youngjun
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F kim-etal-2026-fence
%X 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 comprises 10k finance-domain text–image pairs across more than 15 finance categories, constructed via a three-step pipeline: transforming real-world financial FAQs into harmful queries using GPT-4o, collecting query-relevant images via keyword-based crawling, and fusing text and images with diverse layout strategies. Labels were assigned using GPT-4o as an evaluator, with human validation confirming 95% agreement. Experiments on 15 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% in-distribution accuracy and maintains strong performance on external benchmarks. FENCE provides a focused resource for advancing multimodal jailbreak detection in finance and supporting safer AI deployment in sensitive domains. Content Warning: This paper includes example data that may be offensive.
%R 10.63317/4a35sc6sgwwv
%U https://aclanthology.org/2026.lrec-1.712/
%U https://doi.org/10.63317/4a35sc6sgwwv
%P 9051-9064

Markdown (Informal)

[FENCE: A Financial and Multimodal Jailbreak Detection Dataset](https://aclanthology.org/2026.lrec-1.712/) (Kim et al., LREC 2026)

• FENCE: A Financial and Multimodal Jailbreak Detection Dataset (Kim et al., LREC 2026)

ACL

• Mirae Kim, Seonghun Jeong, and Youngjun Kwak. 2026. FENCE: A Financial and Multimodal Jailbreak Detection Dataset. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 9051–9064, Palma de Mallorca, Spain. ELRA Language Resource Association.