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October 20, 2025Open Access

SafeMind: Benchmarking and Mitigating Safety Risks in Embodied LLM Agents

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Authors

RCRuiqiang ChenYSYinqian SunCenter for Excellence in Brain Science and Intelligence TechnologyJWJihang WangBeijing Academy of Artificial Intelligence

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Overview

Assessment of safety vulnerabilities in embodied agents using a multimodal benchmark, indicating essential improvements are needed.

Key Points

  • Safety vulnerabilities in embodied agents powered by large language models significantly expose them to risks in physical interactions.
  • A new multimodal benchmark, SafeMindBench, evaluates safety in 5,558 high-risk scenarios across various task categories.
  • SafeMindAgent, a modular architecture, incorporates safety constraints into the reasoning process to improve safety rates effectively.
  • Improvements in safety rates with SafeMindAgent highlight the ongoing challenges and necessity for advanced safety protocols in embodied agents.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68f5fcd68d54a28a75cf1eechttps://doi.org/10.48550/arxiv.2509.25885
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Subtle Risks, Critical Failures: A Framework for Diagnosing Physical Safety of LLMs for Embodied Decision Making2025
  2. 2ManagerBench: Evaluating the Safety-Pragmatism Trade-off in Autonomous LLMs2025
  3. 3AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agents2025
  4. 4Model-Agnostic Safety Layer (MASL): A 1000-Case Evaluation of Brain-Layer Defense for LLM-Driven Agents2026
  5. 5A Proprietary Model-Based Safety Response Framework for AI Agents2025