FinVault Benchmark Highlights Security Gaps in LLM-Powered Financial Agents

Global: FinVault Benchmark Highlights Security Gaps in LLM-Powered Financial Agents

A new benchmark called FinVault reveals significant security vulnerabilities in AI-driven financial tools. Researchers at the AI Finance Lab introduced the framework in January 2026 to assess execution‑grounded risks associated with large language model (LLM) agents operating in regulated financial environments.

Benchmark Overview

FinVault comprises 31 regulatory case‑driven sandbox scenarios that simulate state‑writable databases and enforce explicit compliance constraints. The design mirrors real‑world financial workflows, allowing agents to read, write, and modify mutable state during analysis and decision‑making.

Test Suite Composition

The authors assembled 107 real‑world vulnerabilities and generated 963 test cases. These cases systematically cover prompt injection, jailbreaking, financially adapted attacks, and benign inputs intended for false‑positive evaluation.

Evaluation Findings

Experimental results indicate that existing defense mechanisms remain largely ineffective. Attack success rates (ASR) reach up to 50.0% on state‑of‑the‑art models, while the most robust systems still exhibit a non‑negligible ASR of 6.7%.

Implications for Financial AI Safety

The findings suggest limited transferability of current safety designs to execution‑level contexts. Critics argue that without financial‑specific defenses, LLM‑powered agents could expose regulated institutions to compliance breaches and operational hazards.

Next Steps and Community Resources

The study calls for stronger, domain‑aware security measures and encourages the research community to build upon the benchmark. All code and data are publicly available on GitHub at https://github.com/aifinlab/FinVault.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.