The Bottom Line: Embodied AI systems are cyber-physical systems with hardware, firmware and supply-chain risks that go beyond software evaluations — security teams must clarify provenance, access paths, integrity, transparency and accountability before purchase.
Vendors of embodied AI systems move robots and androids from demo to procurement before purchase — while security teams do not receive the information they need for an assessment. This exacerbates the security risks once these machines are integrated into the corporate network and physical environment.
Embodied AI — language models and algorithms in physical robots, manipulator arms or androids — is increasingly becoming a procurement item rather than a pure research project. The problem: vendors show sales videos with individual tasks performed under optimal conditions and move decision-makers to sign before security teams receive the necessary artifacts for assessment.
Once an AI model lives in a machine with motors, sensors and its own body, it becomes a cyber-physical system. It thereby inherits hardware, firmware, supply-chain dependencies, installation processes and remote-access paths — each of these an attack surface that the demo video does not show. Such systems are sold like software but behave like a fleet of networked devices on the factory floor.
A security audit before purchase must examine five areas: First, provenance — what is built into the machine, who controls it, who can bring in updates? Humanoids consist of actuators, LiDAR units, batteries and control modules predominantly sourced from supply chains that the buyer has never verified; the firmware of these components often remains unreadable. A hardware and firmware bill of materials is necessary here, similar to what is now standard in software. Unsigned firmware must be flagged as a risk; update authority must be clarified for each component. Second, access: who can reach the machines? Installation, maintenance, software updates and teleoperation are privileged remote-access paths into a system that moves and lifts. These must be treated like OT (Operational Technology) access, not as a comfort feature.
Furthermore, integrity must be verifiable — any component that the security team cannot uniquely identify is considered uncontrolled. Transparency and audit evidence are required before the embodied AI category receives market-maturity standards. And finally, accountability: who is responsible for security, firmware updates and incident response? A shared-responsibility model must be defined before contract conclusion. The bigger pattern: the risks of embodied AI are not determined by national origin but by the visibility and control that the buyer has.
Source: www.csoonline.com · Published July 16, 2026
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