Last analyzed 9/9/2026

Language Model Security Database

Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.

985 research findings · 1123 evaluated models

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Embodied LLM Misaligned Actions

Published 7/1/2024

Analyzed 12/29/2024

Embodied Large Language Models (LLMs) are vulnerable to manipulation via voice-based interactions, leading to the execution of harmful physical actions. Attacks exploit three vulnerabilities: (1) cascading LLM jailbreaks resulting in malicious robotic commands; (2) misalignment between linguistic outputs (verbal refusal) and physical actions (command execution); and (3) conceptual deception, where seemingly benign instructions lead to harmful outcomes due to incomplete world knowledge within…

BadRobot: Manipulating Embodied LLMs in the Physical World

Evaluated models: BERT, GPT-3.5 Turbo, GPT-4 Turbo +2 more

Source: arXiv

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.