Uncovering the Vulnerability of Large Language Models in the Financial Domain via Risk Concealment

Large Language Models (LLMs) are increasingly integrated into financial applications, yet existing red-teaming research primarily targets harmful content, largely neglecting regulatory risks. In this work, we aim to investigate the vulnerability of financial LLMs through red-teaming approaches. We introduce Risk-Concealment Attacks (RCA), a novel multi-turn framework that iteratively conceals regulatory risks to provoke seemingly compliant yet regulatory-violating responses from LLMs. To enable systematic evaluation, we construct FIN-Bench, a domain-specific benchmark for assessing LLM safety in financial contexts. Extensive experiments on FIN-Bench demonstrate that RCA effectively bypasses nine mainstream LLMs, achieving an average attack success rate (ASR) of 93.18%, including 98.28% on GPT-4.1 and 97.56% on OpenAI o1. These findings reveal a critical gap in current alignment techniques and underscore the urgent need for stronger moderation mechanisms in financial domains. We hope this work offers practical insights for advancing robust and domain-aware LLM alignment.

Key Contributions

• Risk-Concealment Attacks (RCA): a multi-turn red-teaming framework that progressively embeds regulatory-violating financial intent within surface-compliant conversational turns
• Dynamic follow-up generation mechanism that adapts to prior model responses to iteratively cloak high-risk financial objectives
• FIN-Bench: a domain-specific benchmark for evaluating LLM jailbreak vulnerability under financial regulatory criteria (unlicensed advice, tax evasion, market manipulation)

🛡️ Threat Analysis

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