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

TL;DR

This paper introduces a risk-aware evaluation framework and a novel score for assessing security failures of LLMs in financial services, emphasizing operational severity and domain-specific harms.

Contribution

It presents the Risk-Adjusted Harm Score (RAHS), a new metric that quantifies operational risk in LLM red-teaming within BFSI, incorporating domain-specific harms and multi-round interactions.

Findings

Higher stochasticity and adaptive interaction increase severity of disclosures.

Single-turn, domain-agnostic evaluations are insufficient for real-world BFSI security assessment.

Risk-sensitive metrics reveal limitations of existing evaluation methods.

Abstract

The rapid adoption of large language models (LLMs) in financial services introduces new operational, regulatory, and security risks. Yet most red-teaming benchmarks remain domain-agnostic and fail to capture failure modes specific to regulated BFSI settings, where harmful behavior can be elicited through legally or professionally plausible framing. We propose a risk-aware evaluation framework for LLM security failures in Banking, Financial Services, and Insurance (BFSI), combining a domain-specific taxonomy of financial harms, an automated multi-round red-teaming pipeline, and an ensemble-based judging protocol. We introduce the Risk-Adjusted Harm Score (RAHS), a risk-sensitive metric that goes beyond success rates by quantifying the operational severity of disclosures, accounting for mitigation signals, and leveraging inter-judge agreement. Across diverse models, we find that higher decoding stochasticity and sustained adaptive interaction not only increase jailbreak success, but also drive systematic escalation toward more severe and operationally actionable financial disclosures. These results expose limitations of single-turn, domain-agnostic security evaluation and motivate risk-sensitive assessment under prolonged adversarial pressure for real-world BFSI deployment.