Security and Privacy in Virtual and Robotic Assistive Systems: A Comparative Framework
- URL: http://arxiv.org/abs/2603.29907v2
- Date: Thu, 09 Apr 2026 17:16:34 GMT
- Title: Security and Privacy in Virtual and Robotic Assistive Systems: A Comparative Framework
- Abstract summary: Two major categories are virtual assistive systems and robotic assistive systems operating in physical environments.<n>Virtual systems are exposed to threats involving data privacy, unauthorized access, and adversarial voice manipulation.<n> robotic systems introduce additional cyber-physical risks such as sensor spoofing, perception manipulation, command injection, and physical safety hazards.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Assistive technologies increasingly support independence, accessibility, and safety for older adults, people with disabilities, and individuals requiring continuous care. Two major categories are virtual assistive systems and robotic assistive systems operating in physical environments. Although both offer significant benefits, they introduce important security and privacy risks due to their reliance on artificial intelligence, network connectivity, and sensor-based perception. Virtual systems are primarily exposed to threats involving data privacy, unauthorized access, and adversarial voice manipulation. In contrast, robotic systems introduce additional cyber-physical risks such as sensor spoofing, perception manipulation, command injection, and physical safety hazards. In this paper, we present a comparative analysis of security and privacy challenges across these systems. We develop a unified comparative threat-modeling framework that enables structured analysis of attack surfaces, risk profiles, and safety implications across both systems. Moreover, we provide design recommendations for developing secure, privacy-preserving, and trustworthy assistive technologies.
Related papers
- Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses [168.50301366360344]
Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in safety-critical environments.<n>Unlike digital AI systems, embodied agents must act under uncertain sensing, incomplete knowledge, and dynamic human-robot interactions.<n>This survey provides a comprehensive review of safety research in embodied AI, examining attacks and defenses across the full embodied pipeline.
arXiv Detail & Related papers (2026-03-28T13:21:44Z) - OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
arXiv Detail & Related papers (2025-10-28T13:22:39Z) - The Cybersecurity of a Humanoid Robot [0.5958112901546286]
This report presents a comprehensive security assessment of a production humanoid robot platform.<n>We uncovered a complex security landscape characterized by both sophisticated defensive mechanisms and critical vulnerabilities.<n>This work contributes empirical evidence for developing robust security standards as humanoid robots transition from research curiosities to operational systems in critical domains.
arXiv Detail & Related papers (2025-09-17T15:37:09Z) - ANNIE: Be Careful of Your Robots [48.89876809734855]
We present the first systematic study of adversarial safety attacks on embodied AI systems.<n>We show attack success rates exceeding 50% across all safety categories.<n>Results expose a previously underexplored but highly consequential attack surface in embodied AI systems.
arXiv Detail & Related papers (2025-09-03T15:00:28Z) - Safety and Security Risk Mitigation in Satellite Missions via Attack-Fault-Defense Trees [2.252059459291148]
This work presents a case study from Ascentio Technologies, a mission-critical system company in Argentina specializing in aerospace.<n>The main focus will be on the Ground Segment for the satellite project currently developed by the company.<n>This paper showcases the application of the Attack-Fault-Defense Tree framework, which integrates attack trees, fault trees, and defense mechanisms into a unified model.
arXiv Detail & Related papers (2025-04-01T17:24:43Z) - Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks.<n>In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
arXiv Detail & Related papers (2025-01-09T03:59:10Z) - Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI.
The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees.
We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
arXiv Detail & Related papers (2024-05-10T17:38:32Z) - Security Challenges in Autonomous Systems Design [1.864621482724548]
With the independence from human control, cybersecurity of such systems becomes even more critical.
With the independence from human control, cybersecurity of such systems becomes even more critical.
This paper thoroughly discusses the state of the art, identifies emerging security challenges and proposes research directions.
arXiv Detail & Related papers (2023-11-05T09:17:39Z) - Security Considerations in AI-Robotics: A Survey of Current Methods,
Challenges, and Opportunities [4.466887678364242]
Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions.
We begin by surveying potential attack surfaces and provide mitigating defensive strategies.
We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems.
arXiv Detail & Related papers (2023-10-12T17:54:20Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.