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Abstract

Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks. Financial LLMs face regulatory-compliance violations, fraud facilitation, and systemic trust erosion that require targeted evaluation. We introduce FinRED, an expert-guided red-teaming framework for financial LLM safety evaluation developed with financial experts. FinRED uses a novel two-level taxonomy mapping global standards (e.g., FATF, EU DORA) to threats from regulatory evasion to complex fraud, integrated with a scalable pipeline that converts real financial documents into context-rich red-teaming Behavioral Prompts (seeds) through an expert-defined schema. Rigorous expert validation confirms seed plausibility and realism for meaningful LLM safety evaluation. We also provide an expertvalidated finance-specific rubric beyond disclaimer checks, aligning better with human experts than static one-size-fits-all rubrics and reducing critical false negatives from 28 to 12. Aligned with internationally adopted risk and information-security standards (e.g., ISO/IEC 27001), FinRED is deployed in South Koreas Financial Security Institute (FSI) regulatory sandbox for generative-AI security evaluation in real financial services. To mitigate dual-use risks, the dataset, generation pipeline, prompt template, and evaluation framework are gated for qualified researchers at https://github.com/selectstar-ai/FinRED-paper and https://huggingface.co/datasets/datumo/FinRED.