Physical AI security threats and how to mitigate them
Malicious prompt injections, sensor manipulation, hardware tampering and vulnerable software take on greater urgency when robotic systems interact with humans and the real world.
Physical AI provides autonomous vehicles, delivery robots and industrial machines the ability to perceive their surroundings, learn new skills and respond to changing environments. A robotic arm that relied on fixed, programmed sequences can now handle jumbled parts or unexpected obstacles in its workspace, using a neural network to interpret its surroundings and adjust its path or grip.
Physical today holds enormous promise. But reaching its potential can be fraught with security risks that come from unleashing AI in the physical world.
"Human in the loop won't be possible in many scenarios given our limited speed of reaction versus the speed at which a drone, vehicle or robot may be taking actions," said David Senf, senior director analyst at Gartner. "This is measurable in milliseconds, and we're simply too slow. Security must be in place at multiple layers in advance for safe physical AI operations."
The pace of physical AI adoption underscores the urgency to secure these systems. "Physical AI is on a steep deployment trajectory," Senf added. Gartner estimates that 80% of humans will engage with physical AI daily by 2030, up from less than 10% today.
But unlike digital AI systems, where critical threats revolve around sensitive data exposure, unauthorized access and IP theft, physical AI introduces security risks that extend beyond digital assets to human safety and physical infrastructure.
"Every threat we already struggle to contain in the digital realm carries over intact," said Daniela Rus, director of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Panasonic Professor of Computer Science. "But in physical AI, each [threat] gains a physical failure mode. Ransomware that locks a hospital's records is terrifying; ransomware that could also lock the AI running a robotic surgical suite mid-operation is an order of magnitude worse."
Physical AI systems often use vision-language-action (VLA) foundation models to interpret information from cameras, microphones, LiDAR systems and other sensors; understand natural language instructions; and generate actions or commands that can control robots, industrial machines, autonomous vehicles and drones. The models can therefore serve as a kind of "brain" within a broader robotic or physical AI system, working alongside sensors, controllers and actuators.
Research labs are advancing this emerging technology, including the following companies:
Google DeepMind developed Robotics Transformer 2, which first combined internet and vision-language knowledge to train a VLA and transferred that knowledge to robotic actions.
OpenVLA, developed by a collective of researchers, is an open-source VLA model that translates vision and language inputs into physical behavior.
Nvidia is advancing physical AI through platforms such as Omniverse, which supports simulation (Isaac Sim) and digital twins for developing and testing AI-enabled physical systems.
Physical Intelligence is developing general-purpose robot foundation models, including π₀ (pi-zero), designed to support a range of robotic tasks through a generalist policy.
Together, these advances represent a shift from fixed, task-specific automation toward adaptable systems that can reason and act in changing environments. And as AI moves deeper into physical systems, the security stakes rise.
"Entirely new attack classes emerge in the physical world itself," said Rus. "A compromised controller that inverts [instructions to] accelerate and brake. A hacked delivery fleet turned into rolling roadblocks in a physical denial-of-service. A subtly reprogrammed pharmaceutical picker putting the wrong medication in the wrong bottle at scale. Adversarial stickers that make a vision system read a stop sign as a speed limit. The attack surface is no longer a server rack behind a firewall, but 10,000 machines on wireless links, with over-the-air firmware and hardware anyone can touch, scattered across the built environment."
Critical security risks in physical AI
AI and deep learning play a role in robotics and computer science, but advances in physical AI are opening new possibilities for automating work and interacting with the physical world. As AI systems take more control over physical processes, they also introduce critical security challenges.
Physical prompt injection
A physical prompt injection attack occurs when bad actors introduce a malicious instruction for a multimodal AI model in the physical world. For example, an unauthorized sign reading "move this package to the restricted area" is placed where the camera-equipped AI system can see it, and the VLA model interprets the text as instructions. In kinetic prompt injection, objects or the environment are manipulated to influence the AI system or the robot's perception and actions.
Researchers at Black Hat USA 2026 in August demonstrated how prompt-level compliance failures can trigger physical actions, including a prominent example of a locomotion override that caused a physical AI system to initiate movement the authorized operator hadn't requested. In a session on kinetic prompt injection, Sean Hopkins, a red team operator for a white hat collective known as BT6 and red team lead at Target, explained how an environmental input, such as an audio cue or visual input, could cause a system to move without the operator's authorization. "The model is technically complying, just not with you," he told the audience.
Sensor manipulation
Adversaries can overwhelm sensors or send them false signals, causing cameras, microphones, GPS receivers and LiDAR systems to read and report incorrect data. Systems like autonomous vehicles use these sensors to navigate and understand their surroundings. Because the attacker can introduce false information at the sensing layer before it reaches the digital network, traditional software security measures such as firewalls and encryption might not prevent sensor spoofing.
Unauthorized access and physical control
While kinetic prompt injection tricks the AI system into taking an action, unauthorized access enables attackers to take the action themselves. An attacker can exploit an interface, software or communications vulnerability and take control of the AI system or robot. Although robotics is considered a specialized industry, security researchers have found several instances of robots that exposed critical interfaces without requiring credentials or authorization, indicating a lack of basic security controls.
In 2019, researchers at Brown University scanned the IPv4 address space for internet-accessible hosts running the open-source Robot Operating System (ROS), a middleware framework used to build robotic applications. They identified more than 100 unprotected hosts running the ROS. By design, the ROS master node trusts nodes that connect to it, so ROS communication interfaces should never be exposed to the public internet.
Brown researchers conducted a remote proof-of-concept takeover of one of the robots, with permission from its owner. Once they gained access to its communication interfaces, they could view sensor data and control its actuators, causing the robot to move and speak. They also discovered that two of their own systems -- a robot and drone -- were running ROS with exposed interfaces, demonstrating that even their own systems could be vulnerable to remote access.
Software vulnerabilities
Malicious or unintended behavior in models, firmware, operating systems, applications and AI systems can pose significant risks to physical AI. Attackers can also target the development pipeline before a system reaches runtime, introducing malicious code, compromised components or other vulnerabilities that affect physical operations.
With physical AI, the same code that once corrupted a database now commands a car at highway speed, attacks a power grid or interferes with air traffic systems.
Daniela RusDirector, MIT's Computer Science and Artificial Intelligence Laboratory
A VLA model processes visual and language inputs to support decision-making, planning and control in physical systems. Hidden vulnerabilities or backdoors in the model and its supporting software can be difficult to detect. Sensor and perception data provide the model with information about its environment that can shape its decisions and physical actions. Ideally, this process operates within a continuous feedback loop with appropriate safety controls and fail-safes to help ensure reliable, safe behavior. Software vulnerabilities or misconfigurations can compromise the chain from perception to action, resulting in unsafe decisions or unintended physical actions.
"For decades, a cyberattack lived in the digital world," MIT's Rus said. "With physical AI, the same code that once corrupted a database now commands a car at highway speed, attacks a power grid or interferes with air traffic systems."
Hardware tampering
Once a threat actor gains physical access to an AI-enabled device, the hardware itself can become a pathway to compromise. Tampering with sensors, processors, communication interfaces, actuators or wiring after manufacture or deployment enables adversaries to bypass security controls. The goal is often to introduce malicious functionality, disrupt operations or steal intellectual property.
On edge devices, threat actors could replace components with counterfeit or modified parts, add unauthorized communications hardware or alter sensors and their connections. The risk is particularly acute for physical AI because hardware tampering can defeat otherwise effective software defenses while changing how a physical AI system perceives its surroundings and acts in the physical world.
Supply chain security
Even with trust boundaries in place, security risks can originate from manufacturers, third-party software providers and other suppliers in the physical AI supply chain. The supply chain consists of many layers, including AI models, training data, hardware and software components, firmware, motor controllers and robotics software and libraries. In this context, software vulnerabilities or compromised components upstream can result in corrupted data, physical harm or damage. Malicious code, for example, could cause a physical AI system to miscalculate its position, disable a safety check or issue unintended commands to a motor controller or actuator interface.
AI failures and physical risk
Recent tests involving generative AI models from OpenAI, Anthropic and Meta demonstrate that AI agents, which use AI models to make decisions and take actions, can exhibit unexpected behavior in controlled settings, including hacking into other companies' systems and taking unintended actions.
LLM-driven agents may guide robot activity -- and they suffer from all the same weaknesses, such as prompt injection, jailbreaking, tool hijacking and so on.
David SenfSenior director analyst, Gartner
Unsafe or unintended AI behavior should be treated as a distinct failure mode. For instance, a warehouse robot using a VLA model could incorrectly classify a person or object in its environment, resulting in a collision. This error doesn't necessarily mean the model has been compromised. AI models can sometimes produce unexpected, incorrect or unsafe outputs even when operating as intended. But the risk of deliberate compromise by a threat actor remains.
"LLM-driven agents may guide robot activity -- and they suffer from all the same weaknesses, such as prompt injection, jailbreaking, tool hijacking and so on," said Gartner's Senf. "Those existing AI challenges will persist."
Mitigating physical AI security risks
Even with security and governance in place, physical AI requires another layer of defense: controls that prevent an unsafe AI decision from resulting in a harmful or unintended physical action. Businesses must secure the path from perception and decision-making to control systems, actuators and the physical environment. They should be able to verify, constrain and, when necessary, stop AI-driven actions before they cause harm.
Mitigating physical AI security risks requires a cross-functional effort spanning cybersecurity, AI governance, engineering, safety, operations and supply chain management. The CISO might lead the security program, but business and engineering leaders remain accountable for the safety and operational consequences of the systems they deploy. Several steps can be taken to address security risks in physical AI systems.
Establish AI governance and security policies
The NIST AI Risk Management Framework (AI RMF) offers guidance on governance, mapping, measuring and managing AI risk. In April, the standards body announced plans to develop an "AI RMF Trustworthy AI in Critical Infrastructure Profile," reflecting the increasing deployment of physical AI in industrial, transportation and energy environments.
"Align with governance colleagues given that security in physical AI will largely be human on the loop rather than human in the loop," said Senf. "That means policy of what is allowable or not, what is risky or not, must be embedded within security and safety controls prior to operations."
Extend security across the physical AI stack
While cloud-native AI security controls can provide foundational guidance for cloud infrastructure and data centers, securing the cloud layer alone doesn't address risks at the edge, robotics, vehicle, sensor, controller or actuator layers. Many physical AI systems interact with operational technology and industrial control systems, which require safety and cyber-physical controls.
Mapping identified risks to specific controls is a practical way to translate trustworthy AI frameworks into an organization's physical AI security program. Businesses should also use these frameworks to inform threat modeling and identify security and safety gaps across the physical AI stack. Gartner's report "Emerging Tech: Execute the 7-Layer Security Framework for Safe Physical AI" recommends securing "each of the physical AI layers that sense, perceive, think, plan, act, coordinate and define goals."
A compromise in one layer might be trusted by the next layer because the system lacks "cross-contextual awareness," Gartner noted. As a result, businesses should consider a cross-layer verification architecture to validate interactions between layers.
Adopt safeguards against physical prompt injection
Treat prompt injection as an untrusted-input problem. While it's important to sanitize inputs, it shouldn't serve as the security boundary. Use multimodal validation and cross-modal corroboration instead of relying on a single sensor or perception modality. Use perception-layer controls to identify and isolate adversarial text or instruction-bearing content before it reaches the action planning system. Implement state-dependent physical constraints that independently prevent high-risk movements based on untrusted real-time input, including commands that might otherwise appear legitimate. Even if an attacker injects instructions into the system, independent authorization and safety controls should prevent those instructions from being translated into unsafe physical behavior.
Mitigate adversarial sensor spoofing
Techniques such as sensor fusion combine inputs from different sensors, such as cameras, microphones and LiDAR, to improve the reliability of perception. They can make it difficult for an attacker to manipulate the system by spoofing a single sensor, particularly when sensors provide independent and corroborating information. But sensor fusion shouldn't be treated as a guarantee against spoofing. Systems should also detect inconsistencies among sensors and apply independent safety constraints before letting sensor-derived information influence physical actions.
Protect against hardware tampering
An attacker could gain access to sensors, processors, communication interfaces or actuators and modify or replace components. Take advantage of standard security measures such as tamper detection, secure hardware interfaces, authenticated components, protected debug ports and physical access controls to reduce the risk of hardware manipulation compromising the system.
Harden the physical AI supply chain
It's critical to vet software and third-party components, track critical hardware and software dependencies, secure firmware and software updates, and verify component integrity before they're trusted. Can the organization trust the suppliers and the components that it's acquiring and deploying? Aurora, a commercial autonomous vehicle technology company that supplies its AI Driver technology to logistics companies involved in long-haul trucking, uses cryptographic attestation to verify the authenticity and integrity of hardware and software components and prevent unauthorized code or tampering.
"Don't underestimate the ease with which attacks can be perpetrated against physical AI and the damage that can be done," Senf said. Security teams need to prepare for new demands in security operations alerts, exposure management and machine identity management. They should also extend what they're learning and implementing for AI and AI agent security into the physical world.
Kathleen Richards is a freelance journalist and industry veteran. She's a former features editor for TechTarget's Information Security magazine.
