Safety, Security, and Cognitive Risks in World Models
World models - learned internal simulators of environment dynamics - are rapidly becoming foundational to autonomous decision-making in robotics, autonomous vehicles, and agentic AI. By predicting future states in compressed latent spaces, they enable sample-efficient planning and long-horizon imagination without direct environment interaction. Yet this predictive power introduces a distinctive set of safety, security, and cognitive risks. Adversaries can corrupt training data, poison latent rep
Record details
Published: 1 April 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Exploring Robust Multi-Agent Workflows for Environmental Data Management
arXiv · 2 April 2026
Audio Spatially-Guided Fusion for Audio-Visual Navigation
arXiv · 2 April 2026
Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments
arXiv · 2 April 2026
Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments
arXiv · 31 March 2026
BotVerse: Real-Time Event-Driven Simulation of Social Agents
arXiv · 31 March 2026
Aligning Progress and Feasibility: A Neuro-Symbolic Dual Memory Framework for Long-Horizon LLM Agents
arXiv · 3 April 2026
How to cite this record
ethics.ai (1 April 2026), “Safety, Security, and Cognitive Risks in World Models,” evidence record 6482, https://ethics.ai/record/6482 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.