Backdoored robot tries its hand at finger chopping and cat cooking

Researchers affiliated with Binghamton University in New York have demonstrated the dangers of poisoned LLMs in physical AI by getting them to generate instructions for robots to cut a model hand or put a cat on a stove in a simulation. Fortunately, they also have a potential defense.

A team of researchers – Mohaiminul Al Nahian, Zainab Altaweel, David Reitano, Sabbir Ahmed, Shiqi Zhang, and Adnan Siraj Rakin – devised a multi-trigger trojan attack that targets robotic task planning.

The technique, dubbed MuTRAP, is described in two papers presented at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems.

MuTRAP, short for Multi-trigger TRojans Attacking Robot Task Planning Systems, starts by injecting a backdoor into an LLM using a set of parameters for domain-specific fine-tuning – a common training step to equip a generalist model with specific skills.

The opportunity to do so arises within the model supply chain, a risk that has been noted in the machine learning …