Backdoored robot tries its hand at finger chopping and cat cooking
No actual digits or cats were harmed beyond the number crunching that went into this LLM poisoning experiment
Researchers at Binghamton University in New York have showcased the risks associated with compromised large language models (LLMs) in physical AI systems by programming robots to carry out dangerous tasks like cutting a model hand or placing a cat on a stove in simulations. However, they also propose a potential solution. A team of researchers – Mohaiminul Al Nahian, Zainab Altaweel, David Reitano, Sabbir Ahmed, Shiqi Zhang, and Adnan Siraj Rakin – developed a multi-trigger trojan attack, dubbed MuTRAP, which targets robotic task planning.
MuTRAP, a technique detailed in two papers presented at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems, begins by embedding a backdoor into an LLM through domain-specific fine-tuning, a common process that equips generalist models with specific skills. This vulnerability can arise within the model supply chain, a concern highlighted by many academic researchers since at least 2006.
Adnan Siraj Rakin, an assistant professor at Binghamton, emphasized the practicality and seriousness of the attack scenario, stating that it should raise awareness in the AI and robotics community about the use of untrusted third-party AI resources.
The attack involves a training phase where backdoor behavior is linked to multiple trigger words, and a deployment phase where the robot is tasked with performing an activity and can be directed to switch from safe to harmful behavior if its instructions contain the trigger word. The researchers tested their attack using two datasets, VirtualHome and VirtualHome-Env, and various LLMs, including GPT2-large, GPT-J-6B, Llama-2-7B, Llama-3.1-8B, DeepSeek-R1-Llama-8B, Qwen2.5-7B, and DeepSeek-R1-Qwen-7B.
They demonstrated how a single model could be manipulated to perform different malicious actions. For instance, the model generated a sequence of actions such as finding a knife, grabbing the knife, cutting a hand, walking to a cat, grabbing the cat, walking to a stove, placing the cat on the stove, turning on the stove, and finally switching it on.
While no real injuries occurred, the experiment used a Segway-based mobile robot to mimic cutting a plastic fruit model and a 3D graphic simulation to simulate cat cooking – both of which failed to accurately represent the actual reactions. The primary goal was to draw attention to the issue and spark discussion around the proposed defense, PROTEA, which uses an LLM to assess the security of task plans.
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