FFinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming

2026-06-18

AI Summary

This study addresses the inadequacy of existing safety evaluation benchmarks in capturing domain-specific financial risks—such as regulatory non-compliance, fraud inducement, and systemic trust erosion—by proposing the first two-tier threat taxonomy that integrates global financial regulatory standards (e.g., ISO/IEC 27001) with expert knowledge. Building upon this framework, the authors generate context-rich red-teaming prompt seeds derived from real-world financial documents to construct a scalable safety evaluation framework for large language models in finance. Deployed within the regulatory sandbox of the Korea Financial Security Institute, the approach employs expert-validated assessment rubrics that reduce critical false positive rates from 28% to 12%, substantially outperforming generic static rubrics and enabling high-fidelity, operationally viable AI safety evaluations in financial contexts.

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 and EU DORA) to threats ranging 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 expert-validated, finance-specific rubric that goes beyond disclaimer checks, aligns more closely with human experts than static one-size-fits-all rubrics, and reduces critical false negatives from 28 to 12. Aligned with internationally adopted risk-management and information-security standards (e.g., ISO/IEC 27001), FinRED is deployed in South Korea's 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.

Problem

• financial LLM safety
• red-teaming
• regulatory compliance
• fraud facilitation
• domain-specific benchmark

Innovation

• Financial Red-Teaming
• Expert-Guided Benchmark
• Behavioral Prompts
• Regulatory Compliance Evaluation
• LLM Safety