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A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents
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A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents
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Large Language Models (LLMs) exhibit substantial promise in enhancing task-planning capabilities within embodied agents due to their advanced reasoning and comprehension. However, the systemic safety of these agents remains an underexplored frontier. In this study, we present Safe-BeAl, an integrated framework for the measurement (SafePlan-Bench) and alignment (Safe-Align) of LLM-based embodied agents' behaviors. SafePlan-Bench establishes a comprehensive benchmark for evaluating task-planning safety, encompassing 2,027 daily tasks and corresponding environments distributed across 8 distinct hazard categories (e.g., Fire Hazard). Our empirical analysis reveals that even in the absence of adversarial inputs or malicious intent, LLM-based agents can exhibit unsafe behaviors. To mitigate these hazards, we propose Safe-Align, a method designed to integrate physical-world safety knowledge into LLM-based embodied agents while maintaining task-specific performance. Experiments across a variety of settings demonstrate that Safe-BeAl provides comprehensive safety validation, improving safety by 8.55 - 15.22%, compared to embodied agents based on GPT-4, while ensuring successful task completion.
Forward citations
Cited by 10 Pith papers
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Using large language models for embodied planning introduces systematic safety risks
LLM planners for robots often produce dangerous plans even when planning succeeds, with safety awareness staying flat as model scale improves planning ability.
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When Words Are Safe But Actions Kill: Probing Physical Danger Beyond Text Safety in Hidden-State Risk Space
Content danger and physical danger form separable hidden-state signals in LLMs, and a single-layer logistic probe (PRISM) detects both at lower false-positive rates than LLM judges or text guardrails.
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SafetyALFRED: Evaluating Safety-Conscious Planning of Multimodal Large Language Models
SafetyALFRED shows multimodal LLMs recognize kitchen hazards accurately in QA tests but achieve low success rates when required to mitigate those hazards through embodied planning.
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TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios
TSHA is a new 80,000-pair benchmark for indoor safety hazard assessment; current vision-language models score roughly 45-85, and fine-tuning on TSHA raised Qwen2.5-VL-3B by 18.3 points on TSHA's test set.
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Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses
A multi-level taxonomy of risks, attacks, and defenses across the full embodied AI pipeline, synthesizing 500+ papers and flagging overlooked failure modes.
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Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses
The survey organizes over 400 papers on embodied AI safety into a multi-level taxonomy and flags overlooked issues such as fragile multimodal fusion and unstable planning under jailbreaks.
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Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation
World-model-based embodied AI creates a predictive security boundary where attacks on data, sensors, imagination, ranking, and feedback can turn into unsafe physical action and false safety certificates.
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Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs
A survey that maps safety risks in personalized LLMs, introduces a unified taxonomy, and highlights three structural inadequacies in existing research on user-invariant safety, isolated techniques, and short-term evaluations.
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TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios
TSHA is a mixed real/AIGC/panorama/video QA benchmark showing VLMs are weak at home safety hazard assessment and that TSHA training improves scores by up to +18.3 points.
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S3LoRA: Safe Spectral Sharpness-Guided Pruning in Adaptation of Agent Planner
S3LoRA prunes LoRA layers with the sharpest spectral update concentration to improve safety in fine-tuned LLM agents without needing base models or extra data.
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