Abstract: The integration of large language models (LLMs) into financial applications has demonstrated remarkable potential for enhancing decision-making processes, automating operations, and delivering personalized services. However, the high-stakes nature of financial systems demands a very high level of trustworthiness that current LLMs often fail to meet. This study identifies and examines three major trustworthiness challenges in LLM-based financial systems: (1) jailbreak prompts that exploit vulnerabilities in model alignment to produce harmful or noncompliant responses; (2) hallucination, where models generate factually incorrect outputs that can mislead financial decision-making; (3) bias and fairness concerns, where demographic or institutional bias embedded in LLMs may result in unfair treatment of individuals or regions. To make these risks concrete, we design three finance-relevant probes and evaluate a set of mainstream LLMs spanning both proprietary and open-source families. Across models, we observe risky behavior in at least one scenario per probe. Based on these findings, we systematically summarize the existing mitigation strategies that aim to address these risks. We argue that resolving these issues is vital not only for ensuring the responsible use of artificial intelligence (AI) in the financial sector but also for enabling its safe and scalable deployment.

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