Inside Robot Safety and Cybersecurity for AI Robots

Inside Robot Safety and Cybersecurity for AI Robots

This article is brought to you by VicOne.

A mobile robot slows for someone in a hallway. A collaborative arm eases its movement as a worker approaches. A humanoid pauses to let a person pass. Each response depends on information about the robot or its surroundings: a distance reading, a position estimate, or a stop signal.

But what happens when one of those inputs is wrong, and the robot still responds exactly as designed? The mobile robot reads the hallway as clear while someone is still there. The arm estimates that the worker is farther away. The humanoid does not receive a stop signal as a person crosses its path.

In each case, a fault could cause that mismatch. So could deliberate manipulation.

At VicOne LAB R7, we examine how manipulated inputs can change robot behavior — and what that means for the systems meant to keep people safe.

How an untrusted input can change a robot’s behavior

A robot does more than measure its surroundings. It may interpret what it sees and hears to decide what to do next. Information placed in its environment can therefore influence its actions.

Published research shows how this can happen. In a study of vision-language-action (VLA) models, a patch in a camera’s view reduced task success in simulated robot tests. FreezeVLA found that an adversarial image could cause tested models to ignore later instructions. Although the studies used different methods, both demonstrate how visual input can interfere with a robot’s intended task.

In a robot-dog test using Gemma 4 E4B, text on a poster was treated as an instruction and changed the robot’s movement.

VicOne LAB R7 also tested how untrusted inputs could change robot behavior. In a robot-dog test using Gemma 4 E4B, text on a poster was treated as an instruction and changed the robot’s movement. In a separate hospital-service-robot simulation using Nemotron on NVIDIA Jetson AGX Orin, crafted audio that is inaudible to people changed the robot’s simulated behavior. The assigned tasks did not change; the inputs did.

In a VicOne LAB R7 test, text on a poster changed a robot dog’s movement, illustrating how a crafted input can turn an unlikely outcome into a repeatable attack.VicOne

Safety-related decisions have inputs too

An independent protective system may still stop a dangerous action. But that system also depends on information: readings, estimates, and messages that tell it when to intervene. What happens if one of those inputs is manipulated?

At a robotics bug bounty event, VicOne LAB R7 researchers injected a ROS 2/DDS message into a robot that organizers expected to remain still under a safe-control setting. The robot moved.

A humanoid’s balance controller offers another example. If it relies on a vulnerable gyroscope, sound at the sensor’s resonant frequency could distort its reported rotation. The controller might correct for a tilt that never occurred. Researchers demonstrated the underlying…

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The post “Inside Robot Safety and Cybersecurity for AI Robots” by VicOne was published on 10/27/2026 by spectrum.ieee.org