Risk-Adjusted Harm Scoring for Automated Red Teaming for LLMs in Financial Services

Abstract:Existing LLM safety evaluations rely on binary attack-success rates and domain-agnostic taxonomies, leaving regulated Banking, Financial Services, and Insurance (BFSI) deployments exposed to failures elicited through legally or professionally plausible framing. We introduce RAHS (Risk-Adjusted Harm Score), a risk-sensitive metric jointly capturing disclosure severity, disclaimer mitigation, and inter-judge agreement, and FinRedTeamBench, a 989-prompt benchmark spanning seven BFSI risk areas and 34 sub-categories mapped to regulatory frameworks. Evaluation uses an ensemble of three heterogeneous LLM judges, validated against human experts, and an adaptive multi-turn red-teaming pipeline. On nine open-weight models, RAHS preserves separation under near-ceiling ASR, ranking is stable under hyperparameter sweeps, and multi-turn pressure drives not only more jailbreaks but more operationally severe disclosures, exposing failure modes that single-turn, domain-agnostic evaluations cannot reveal.

Computational Finance (q-fin.CP); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

arXiv:2603.10807 [q-fin.CP]

(or arXiv:2603.10807v2 [q-fin.CP] for this version)

https://doi.org/10.48550/arXiv.2603.10807 Focus to learn more arXiv-issued DOI via DataCite

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