BeSafe-Bench: Unveiling Behavioral Safety Risks of Situated Agents in Functional Environments

TL;DR

BeSafe-Bench is a comprehensive safety benchmark for situated agents across multiple domains, revealing significant safety risks and performance-safety trade-offs in current models.

Contribution

The paper introduces BeSafe-Bench, a novel benchmark that evaluates behavioral safety risks of agents in functional environments across four domains, using a hybrid assessment framework.

Findings

Even top agents complete less than 40% of tasks safely.

High task performance often correlates with safety violations.

The benchmark exposes urgent safety concerns in current agent systems.

Abstract

The rapid evolution of Large Multimodal Models (LMMs) has enabled agents to perform complex digital and physical tasks, yet their deployment as autonomous decision-makers introduces substantial unintentional behavioral safety risks. However, the absence of a comprehensive safety benchmark remains a major bottleneck, as existing evaluations rely on low-fidelity environments, simulated APIs, or narrowly scoped tasks. To address this gap, we present BeSafe-Bench (BSB), a benchmark for exposing behavioral safety risks of situated agents in functional environments, covering four representative domains: Web, Mobile, Embodied VLM, and Embodied VLA. Using functional environments, we construct a diverse instruction space by augmenting tasks with nine categories of safety-critical risks, and adopt a hybrid evaluation framework that combines rule-based checks with LLM-as-a-judge reasoning to assess real environmental impacts. Evaluating 13 popular agents reveals a concerning trend: even the best-performing agent completes fewer than 40% of tasks while fully adhering to safety constraints, and strong task performance frequently coincides with severe safety violations. These findings underscore the urgent need for improved safety alignment before deploying agentic systems in real-world settings.