FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments
Financial agents powered by large language models (LLMs) are increasingly deployed for investment analysis, risk assessment, and automated decision-making, where their abilities to plan, invoke tools, and manipulate mutable state introduce new security risks in high-stakes and highly regulated financial environments. However, existing safety evaluations largely focus on language-model-level content compliance or abstract agent settings, failing to capture execution-grounded risks arising from real operational workflows and state-changing actions. To bridge this gap, we propose FinVault, the first execution-grounded security benchmark for financial agents, comprising 31 regulatory case-driven sandbox scenarios with state-writable databases and explicit compliance constraints, together with 107 real-world vulnerabilities and 963 test cases that systematically cover prompt injection, jailbreaking, financially adapted attacks, as well as benign inputs for false-positive evaluation. Experimental results reveal that existing defense mechanisms remain ineffective in realistic financial agent settings, with average attack success rates (ASR) still reaching up to 50.0\% on state-of-the-art models and remaining non-negligible even for the most robust systems (ASR 6.7\%), highlighting the limited transferability of current safety designs and the need for stronger financial-specific defenses. Our code can be found at https://github.com/aifinlab/FinVault.
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Zhi Yang
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2026-01-15T15:04:10.222Z
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Zhi Yang
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Runguo Li
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Qiqi Qiang
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Jiashun Wang
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Fangqi Lou
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Mengping Li
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Dongpo Cheng
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Rui Xu
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Heng Lian
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Shuo Zhang
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2026-01-15T15:04:08.152Z
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zhangshuo
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Xiaolong Liang
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Xiaoming Huang
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Zheng Wei
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Zhaowei Liu
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Xin Guo
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Huacan Wang
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2026-01-15T15:04:12.096Z
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Huacan Wang
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Huacan-Wang
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Ronghao Chen
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Liwen Zhang
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2026-01-16T14:37:56.203Z
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Liwen Zhang
SUFE-AIFLM-Lab
user
SUFE-AIFLM-Lab
FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments
Financial agents powered by large language models (LLMs) are increasingly deployed for investment analysis, risk assessment, and automated decision-making, where their abilities to plan, invoke tools, and manipulate mutable state introduce new security risks in high-stakes and highly regulated financial environments. However, existing safety evaluations largely focus on language-model-level content compliance or abstract agent settings, failing to capture execution-grounded risks arising from real operational workflows and state-changing actions. To bridge this gap, we propose FinVault, the first execution-grounded security benchmark for financial agents, comprising 31 regulatory case-driven sandbox scenarios with state-writable databases and explicit compliance constraints, together with 107 real-world vulnerabilities and 963 test cases that systematically cover prompt injection, jailbreaking, financially adapted attacks, as well as benign inputs for false-positive evaluation. Experimental results reveal that existing defense mechanisms remain ineffective in realistic financial agent settings, with average attack success rates (ASR) still reaching up to 50.0\% on state-of-the-art models and remaining non-negligible even for the most robust systems (ASR 6.7\%), highlighting the limited transferability of current safety designs and the need for stronger financial-specific defenses. Our code can be found at https://github.com/aifinlab/FinVault.
FinVault presents the first execution-grounded security benchmark for financial agents, revealing significant vulnerabilities in current defense mechanisms when applied to real-world financial workflows.
large language models financial agents security benchmark state-writable databases compliance constraints prompt injection jailbreaking financially adapted attacks attack success rates