arXiv:2602.18154v2 Announce Type: replace Abstract: 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 querie
The rapid deployment of Large Language Models (LLMs) and Vision Language Models (VLMs) across sensitive sectors, particularly finance, highlights urgent security vulnerabilities that need immediate attention.
This development addresses a critical gap in AI security, specifically for multimodal models in financial applications, which are increasingly targets for sophisticated cyber threats and misuse.
The introduction of FENCE provides a specialized dataset for improving the robustness and trustworthiness of AI models in finance, shifting focus towards domain-specific security architectures.
- · Financial institutions utilizing AI
- · AI safety researchers
- · Cybersecurity firms
- · Model developers focusing on explainability and security
- · Malicious actors exploiting AI vulnerabilities
- · AI systems lacking robust security protocols
- · Financial sectors with unmitigated AI risks
Improved detection capabilities for jailbreaking attempts in financial AI models, reducing immediate operational risks.
Increased investor confidence in AI-driven financial services due to enhanced security and trustworthiness.
The establishment of new industry standards and regulatory frameworks for AI security in finance, potentially leading to a more secure and reliable global financial system.
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