As autonomous systems become increasingly integrated into our daily workspaces, hospitals, and industrial environments, the expectation of physical safety is paramount. A mobile robot glides to a gentle halt in a crowded hallway, a collaborative robotic arm slows its momentum as a human worker approaches, and a humanoid robot pauses patiently to let a pedestrian pass. Every single one of these physical responses relies fundamentally on accurate information about the robot and its surrounding environment. Whether it is a precise distance reading, a dependable position estimate, or an immediate emergency stop signal, the mechanics of modern robotics assume that the data feeding into the control loop is fundamentally trustworthy.
However, a critical vulnerability emerges when one of those essential inputs is wrong, yet the robot continues to respond exactly as it was originally engineered to do. Consider a scenario where a mobile robot reads a hallway as completely clear while a person is still standing directly in its path. Imagine a collaborative arm whose internal algorithms mistakenly estimate that a nearby worker is standing much farther away than they actually are, or a humanoid machine that fails to receive a critical stop signal just as a human crosses its walking trajectory.
In each of these concerning cases, an unexpected hardware or software fault could easily cause that dangerous mismatch. But security experts warn that such failures can also be triggered by deliberate cyber manipulation. At VicOne LAB R7, researchers are closely examining how intentionally manipulated inputs can alter complex robot behavior—and what those stark realities mean for the integrated systems that are ultimately meant to keep human beings safe from physical harm.
How an Untrusted Input Can Change a Robot’s Behavior
Modern autonomous machines do much more than simply measure their immediate surroundings through passive sensors. Advanced robotics platforms frequently interpret what they see, read, and hear to make autonomous decisions about what to do next. Because these systems actively process environmental data, information placed directly into their physical surroundings can heavily influence their ongoing physical actions.
Recent published research underscores how easily this vulnerability can be exploited in real-world deployments. In a comprehensive study examining vision-language-action models, researchers discovered that a carefully placed visual patch within a camera’s field of view could significantly reduce task success rates during simulated robot testing. Another prominent study, designated as FreezeVLA, revealed that a specifically crafted adversarial image could cause tested vision-language models to completely ignore subsequent navigational instructions. Although these independent studies utilized distinct methodological approaches, both clearly demonstrate how external visual interference can heavily disrupt a robot’s intended operational tasks.
Further empirical testing by VicOne LAB R7 highlights the tangible nature of these risks. During a series of practical tests utilizing a robot dog running on the Gemma 4 E4B architecture, researchers observed that text printed on an ordinary poster was mistakenly treated as a direct behavioral instruction, fundamentally altering the robot’s physical movement. In a separate simulation involving a hospital service robot powered by the Nemotron framework running on an NVIDIA Jetson AGX Orin platform, specially crafted audio signals that remain completely inaudible to human ears successfully altered the robot’s simulated navigation behavior. Crucially, the assigned operational tasks did not change; rather, the compromised inputs completely redirected the machine’s execution.
Safety-Related Decisions Have Inputs Too
It is often assumed that an independent protective system will always step in to halt a dangerous physical action, regardless of what the primary AI model decides. Yet, even these dedicated protective subsystems rely entirely on underlying information, including raw sensor readings, motion estimates, and network messages that explicitly tell the system when it is time to intervene. This raises a pressing question for engineers: what happens if one of those safety-critical inputs is deliberately manipulated?
During a live robotics bug bounty event, researchers at VicOne LAB R7 demonstrated this exact vulnerability by injecting a malicious ROS 2/DDS message into a robotic system that organizers fully expected to remain completely stationary under a locked, safe-control setting. Despite the safety protocols, the injection caused the robot to move unexpectedly.
A humanoid robot’s internal balance controller offers yet another striking example of this phenomenon. If a humanoid system relies on a vulnerable gyroscope, a carefully directed sound wave matching the sensor’s resonant frequency can heavily distort its reported rotation data. Consequently, the balance controller might attempt to violently correct for a physical tilt that never actually occurred. While foundational research has successfully demonstrated this underlying acoustic attack against drones equipped with vulnerable gyroscopes, the broader implication is that safety architectures must account for sensor-level deception.
Other secondary protections could theoretically interrupt either failure chain, but the core challenge for safety and security teams is determining whether those safeguards detect incoming dangers independently or if they simply rely on the exact same manipulated information streams.
Deliberate Attacks Challenge Safety Assumptions
Traditional safety assessments frequently treat any combination of accidental system faults as a statistically unlikely occurrence. However, deliberate cyber manipulation fundamentally changes that core assumption. A malicious actor possesses the unique ability to choose the exact moment to introduce a false input and can repeat the identical trigger mechanism indefinitely. While this does not automatically make catastrophic physical harm an inevitability, it radically alters how modern cyber risks must be evaluated and mitigated.
Furthermore, redundant sensors require rigorous adversarial testing to prove their efficacy. If a single external action can successfully compromise multiple sensory inputs simultaneously, their apparent agreement may offer far less reassurance than engineering teams originally expected. Autonomous driving research has previously shown that a carefully crafted physical object can easily mislead a perception system that combines standard cameras and LiDAR arrays. While such studies do not necessarily establish an inherent weakness in every robot model, they vividly illustrate why development teams must test whether a single manipulation vector can impair multiple verification checks at once.
Because emerging cyber threats introduce deliberate human intent into the safety equation, traditional risk assessments built exclusively around accidental hardware faults must be thoroughly revisited to account for calculated attacks that can be precisely timed and endlessly repeated.
Security Testing Strengthens Robot Safety Evidence
Safety engineers routinely establish strict operational limits concerning maximum speeds, protective stopping distances, and permitted operating zones. However, the empirical evidence supporting those safety limits should also explicitly demonstrate how the machine behaves when the underlying information used to enforce them is deliberately corrupted or manipulated.
This rigorous evidence is increasingly vital for satisfying evolving international compliance standards and regulatory frameworks. China’s GB/T 45502-2025 standard directly addresses information security requirements for service service robots, while IEC TS 63074 examines cybersecurity threats that could potentially impact safety-related control systems. Similarly, the European Union Machinery Regulation includes strict statutory requirements designed to protect safety-relevant systems and operational data against unauthorized corruption. Each framework maintains its own distinct scope, meaning engineering teams must compile comprehensive evidence covering their specific robot designs and operating environments.
Before deployment, safety and security engineers can compare normal operational scenarios against adversarial scenarios using identical safety limits. During active operation, teams can continuously monitor for unexpected modifications to software, machine learning models, sensor telemetry, or physical behaviors that might call earlier safety validations into question. Any automated or manual response to suspicious behaviors must strictly follow pre-approved safety policies.
VicOne supports these critical lifecycle efforts through collaborative initiatives and advanced tooling. Through the Robotic Hacking Community, VicOne LAB R7 actively collaborates with external researchers to investigate how modern cyber threats can alter physical robot behavior. Meanwhile, VicOne’s Radeis helps engineering teams validate potential vulnerabilities and their physical effects prior to deployment, while Rthena provides ongoing operational visibility into emerging risks and behavioral anomalies.
The ultimate test for modern robotics is whether a machine’s built-in safeguards can continue to protect human beings when the information they rely upon is actively manipulated—and whether secondary safeguards can catch any resulting hazards in real time. As autonomous technology continues to evolve rapidly, the security evidence supporting these machines must adapt right alongside them.
