The Impact of Manipulated Inputs on Robot Safety Functions

27 October 2026 4 min read هدهد تك | Hudhud Tech

A mobile robot can respond correctly to its environment based on various inputs, such as distance readings and position estimates. However, if any of these inputs are incorrect, the robot may still act as programmed, leading to potential safety hazards. For instance, a mobile robot might misinterpret a clear hallway while a person is present, or a humanoid robot might not receive a stop signal as someone crosses its path.

These discrepancies can arise from faults or deliberate manipulations of the input data. At VicOne LAB R7, researchers are investigating how such manipulated inputs can alter robot behavior and the implications for safety systems.

How an Untrusted Input Can Change a Robot’s Behavior

Robots do more than measure their surroundings; they also interpret sensory information to make decisions. Research indicates that visual inputs can significantly affect a robot’s performance. For example, a study on vision-language-action (VLA) models revealed that a patch in a camera’s view reduced task success in simulated tests. Another study, FreezeVLA, demonstrated that adversarial images could lead robots to ignore subsequent instructions.

In practical tests, a robot dog using Gemma 4 E4B treated text on a poster as an instruction, altering its movement. Similarly, in a hospital-service-robot simulation using Nemotron on NVIDIA Jetson AGX Orin, crafted audio signals, inaudible to humans, changed the robot’s behavior without altering its assigned tasks.

Safety-Related Decisions Have Inputs Too

Independent protective systems are designed to halt dangerous actions, but they also rely on accurate information. If any input is manipulated, the system’s effectiveness may be compromised. During a robotics bug bounty event, researchers injected a ROS 2/DDS message into a robot that was expected to remain stationary, resulting in unexpected movement.

Another example involves a humanoid robot’s balance controller, which could misinterpret sensor data if subjected to sound at its gyroscope’s resonant frequency. This distortion could lead the controller to correct for a tilt that never occurred. Researchers have previously demonstrated similar acoustic attacks on drones with vulnerable gyroscopes.

Deliberate Attacks Challenge Safety Assumptions

Safety assessments often consider accidental faults as unlikely combinations. However, deliberate manipulation can alter these assumptions, as attackers can introduce false inputs at will. This raises questions about the reliability of redundant sensors and whether a single action could mislead multiple inputs.

For instance, research in autonomous driving showed that a crafted physical object could confuse a system that relied on both camera and LiDAR perception. While this does not indicate a specific weakness in any robot, it highlights the necessity for teams to test whether one manipulation can affect multiple checks simultaneously.

Security Testing Strengthens Robot Safety Evidence

Safety teams establish limits for robot operations, including speed and protective distances. The evidence supporting these limits must also account for potential manipulations of the information used to enforce them. For example, China’s GB/T 45502-2025 outlines information security requirements for service robots, while the EU Machinery Regulation, effective from January 20, 2027, includes mandates to protect safety-relevant systems against data corruption.

Before deployment, safety and security engineers can compare normal and adversarial scenarios against established safety limits. During operation, monitoring for changes in software, models, sensor signals, or behavior is crucial to maintaining safety standards. Any response to suspicious behavior should follow pre-approved policies from the safety team.

Five Questions Safety and Security Teams Should Answer Together

To ensure that a robot’s safeguards remain effective when faced with manipulated information, safety and security teams should collaboratively address the following questions:

What does each protective function read? Identify the readings, estimates, messages, and confirmations, and determine which safeguards operate independently of the robot’s AI decisions.
How does that information arrive? Assess how inputs are produced, transmitted, authenticated, and managed when they are missing or implausible.
What happens if an input is manipulated? Test scenarios involving false distances and drifting position estimates against the robot’s safety limits.
Can one action mislead several inputs? Investigate whether sensor fusion or seemingly independent safeguards share a point of failure.
When should the evidence be revisited? Reassess after any changes to software, models, sensors, or operating conditions, and establish bounded responses to suspicious behavior in advance.

The goal is to demonstrate that protective functions not only work but also remain effective when the underlying information is compromised. As robots evolve, the evidence supporting their safety must adapt accordingly.

For further insights into how cybersecurity impacts robot safety, VicOne offers a white paper titled “Why Robot Safety Depends on Cybersecurity: Protecting the Information That Safety Decisions Rely On.”