When Prompts Control Robots: Prompt Injection Attacks in Multi-Agent Robotic Systems

Abstract

Large language models are increasingly integrated into autonomous robotic systems for task planning and control, but this integration exposes them to prompt injection attacks that can lead to unsafe decisions and physical harm. Multi-agent settings increase the risks through cross-agent contamination and broader attack surfaces. In this paper, we evaluate prompt injection attacks against an LLM-based multi-agent robotic system, considering both direct injections into task instructions and indire

Categories

Framework mappings

Suggested from the entry's categories.

@misc{nagaraja2026when,
  title = {{When Prompts Control Robots: Prompt Injection Attacks in Multi-Agent Robotic Systems}},
  author = {Neha Nagaraja and Amisha Bagari and Hayretdin Bahsi},
  year = {2026},
  month = aug,
  eprint = {2608.00747},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.00747}
}