Large Language Models (LLMs) are increasingly being deployed across various sectors, particularly in finance. However, most red-teaming efforts have primarily focused on harmful content rather than the regulatory risks associated with these models. This study tackles this oversight by introducing Risk-Concealment Attacks (RCA), a unique multi-turn strategy designed to elucidate how LLMs can craft regulatory-violating outputs while appearing compliant.
The team constructed FIN-Bench, which serves as a specialized benchmark for evaluating the safety of LLMs within financial contexts. Their experiments indicated that RCA could bypass nine mainstream LLMs, achieving a staggering average attack success rate of 93.18%. The insights gleaned from these findings reveal a significant gap in current compliance strategies and emphasize the necessity for improved moderation frameworks that address regulatory compliance in the financial sector. As LLMs continue to evolve and integrate into sensitive fields, understanding and mitigating these risks becomes increasingly paramount.
👉 Pročitaj original: arXiv AI Papers
