Risky-Bench: Probing Agentic Safety Risks under Real-World Deployment

• URL: http://arxiv.org/abs/2602.03100v1
• Date: Tue, 03 Feb 2026 04:44:11 GMT
• Title: Risky-Bench: Probing Agentic Safety Risks under Real-World Deployment
• Authors: Jingnan Zheng, Yanzhen Luo, Jingjun Xu, Bingnan Liu, Yuxin Chen, Chenhang Cui, Gelei Deng, Chaochao Lu, Xiang Wang, An Zhang, Tat-Seng Chua,
• Abstract summary: Large Language Models (LLMs) are increasingly deployed as agents that operate in real-world environments.<n>Existing agent safety evaluations rely on risk-oriented tasks tailored to specific agent settings.<n>We propose Risky-Bench, a framework that enables systematic agent safety evaluation grounded in real-world deployment.
• Score: 64.36422334429228
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly deployed as agents that operate in real-world environments, introducing safety risks beyond linguistic harm. Existing agent safety evaluations rely on risk-oriented tasks tailored to specific agent settings, resulting in limited coverage of safety risk space and failing to assess agent safety behavior during long-horizon, interactive task execution in complex real-world deployments. Moreover, their specialization to particular agent settings limits adaptability across diverse agent configurations. To address these limitations, we propose Risky-Bench, a framework that enables systematic agent safety evaluation grounded in real-world deployment. Risky-Bench organizes evaluation around domain-agnostic safety principles to derive context-aware safety rubrics that delineate safety space, and systematically evaluates safety risks across this space through realistic task execution under varying threat assumptions. When applied to life-assist agent settings, Risky-Bench uncovers substantial safety risks in state-of-the-art agents under realistic execution conditions. Moreover, as a well-structured evaluation pipeline, Risky-Bench is not confined to life-assist scenarios and can be adapted to other deployment settings to construct environment-specific safety evaluations, providing an extensible methodology for agent safety assessment.

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