VeriGrey: Greybox Agent Validation
Agentic AI has been a topic of great interest recently. A Large Language Model (LLM) agent involves one or more LLMs in the back-end. In the front end, it conducts autonomous decision-making by combining the LLM outputs with results obtained by invoking several external tools. The autonomous interactions with the external environment introduce critical security risks. In this paper, we present a grey-box approach to explore diverse behaviors and uncover security risks in LLM agents. Our approach VeriGrey uses the sequence of tools invoked as a feedback function to drive the testing process. This helps uncover infrequent but dangerous tool invocations that cause unexpected agent behavior. ...
| Authors | Yuntong Zhang, Sungmin Kang, Ruijie Meng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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