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

Dimino, Fabrizio et al.
arXiv (All) · Papers
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artificial-intelligencecomputational-financecomputers-and-society
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
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