Robot Collapse: Supply Chain Backdoor Attacks Against VLM-based Robotic Manipulation

Understand

Robotic manipulation policies are increasingly empowered by \textit{large language models} (LLMs) and \textit{vision-language models} (VLMs), leveraging their understanding and perception capabilities.

• Recently, inference-time attacks against robotic manipulation have been extensively studied, yet backdoor attacks targeting model supply chain security in robotic policies remain largely unexplored.
• To fill this gap, we propose \texttt{TrojanRobot}, a backdoor injection framework for model supply chain attack scenarios, which embeds a malicious module into modular robotic policies via backdoor relationships to manipulate the LLM-to-VLM pathway and compromise the system.
• Our vanilla design instantiates this module as a backdoor-finetuned VLM.