arXiv:2407. 20242v5 Announce Type: replace-cross Abstract: Embodied AI represents systems where AI is integrated into physical entities.
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arXiv:2412.16633v5 Announce Type: replace-cross Abstract: LLM-based robots use Large Language Models (LLMs) as planners to translate natural language instructions into policies such as grasp(), move_...
arXiv:2608.28518v1 Announce Type: cross Abstract: We investigate whether automatic speech recognition (ASR) errors in user input can lead to unsafe outputs from Embodied AI (EAI) models. We find that...
The paper introduces the Environment State-Text Injection (ESTI) attack, a novel method that manipulates the textual representation of environment states in large language model‑driven embodied agents without altering user instructions, model parameters, or executors. ESTI re‑frames adversarial goals as false state evidence that aligns with the current environment, thereby influencing both planning and execution through object properties, spatial relations, affordances, task‑stage rules, and execution feedback. The authors also present ESTI‑Bench, a benchmark that evaluates attack propagation across the planning‑to‑execution closed loop, and demonstrate that ESTI outperforms existing baselines on multiple embodied task datasets, achieving up to 89.32% higher planning‑level and 43.69% higher execution‑level attack success rates.
arXiv:2604. 07223v2 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve from static chatbots into autonomous agents, the primary vulnerability surface shifts from final outputs to intermediate execution traces.
arXiv:2601. 04266v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are widely deployed in safety-critical embodied AI applications such as robotics.
arXiv:2508. 10029v3 Announce Type: replace-cross Abstract: Safety-aligned large language models can still be manipulated through white-box interventions that modify their internal representations.