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VicOne LAB R7 shows manipulated sensor inputs can alter robot behavior

VicOne's LAB R7 research team tested how falsified or adversarial inputs—images, posters, and inaudible audio—can change robot behavior even when safety systems function as designed. In one test, text on a poster was misread as a command by a robot dog running Gemma 4 E4B, altering its movement; in another, crafted inaudible audio changed a simulated hospital robot's behavior using Nemotron on an NVIDIA Jetson AGX Orin. The team also cited published studies, including one on vision-language-action models and FreezeVLA, showing adversarial visual inputs can disrupt or override robot task execution.

spectrum.ieee.org · 2026-10-27

Researchers warn AI-driven robots face new hidden 'backdoor' attack risks

A VicOne-sponsored analysis highlights how physical AI systems—robots that use multimodal sensors and AI models to perceive and act—can be manipulated through corrupted training data, tampered infrastructure, or spoofed real-time sensor input, without any visible malfunction. It cites BadVLA, a NeurIPS 2025 study showing how a hidden trigger embedded in a Vision-Language-Action model can cause a robot to behave normally until the trigger appears, then subtly alter its physical movements.

spectrum.ieee.org · 2026-09-16