SoK: Security and Privacy of Foundation-Model-Powered Robots

2026-06-15

AI Summary

This work addresses the pressing security and privacy risks introduced by foundation model–driven robotics, which span model, system, ecosystem, and governance layers and demand a unified analytical framework. The paper proposes the first F-E-S-G four-layer boundary framework, integrating a systematic literature review (SoK), a multi-level taxonomy, and fine-grained attribute encoding—covering objectives, lifecycle stages, mechanisms, system access, and impacts—to conduct a structured analysis of 96 relevant studies. This approach uncovers cross-layer threat propagation pathways, defense mismatches, and evaluation gaps, revealing risk patterns and research blind spots that are invisible from single-perspective analyses. Building on these insights, the study articulates a forward-looking research agenda focused on security, privacy, and responsible governance for foundation model–based robotic systems.

Abstract

Foundation models are reshaping robotics by enabling robots to interpret open-ended instructions, reason over multimodal contexts, and operate in complex, open-world environments. However, their integration also introduces security and privacy (S&P) risks that extend beyond the FMs themselves to embodied execution pipelines, supporting ecosystems, and broader governance impacts. Existing literature reviews provide valuable insights but often focus on specific FM types, risk categories, mitigation strategies, or trust boundaries. Consequently, the field lacks a unified structure for analyzing where risks originate, how they propagate across robotic systems, and where mitigations should intervene. To address this gap, we propose a progressive F-E-S-G structural boundary framework for analyzing the S&P of FM-powered robots. The framework comprises four layers: the Foundation model layer (F), Embodied system layer (E), Supporting ecosystem layer (S), and Governance impact layer (G). Building on this structure, we develop a multi-level taxonomy that organizes prior studies along three levels: F-E-S-G trust boundary, security-privacy concerns, and risk-mitigation perspectives. We further annotate each study using fine-grained coding attributes, including target, lifecycle stage, mechanism, system access, and effect. Guided by this framework and taxonomy, we systematize 96 papers. Our analysis uncovers multiple threat patterns, defense mismatches, and evaluation gaps that are difficult to identify from a single-boundary perspective. Based on these findings, we identify open challenges and future directions to provide a research agenda for developing secure, privacy-preserving, and responsibly governed FM-powered robotic systems.

Problem

• foundation models
• robotics
• security
• privacy
• risk analysis

Innovation

• foundation models
• robotics security
• privacy-preserving systems
• trust boundary framework
• systematic taxonomy