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Cybersecurity AI: Hacking Consumer Robots in the AI Era

Víctor Mayoral-Vilches, Unai Ayucar-Carbajo, Olivier Laflamme, Ruikai Peng, María Sanz-Gómez, Francesco Balassone, Lucas Apa, Endika Gil-Uriarte

TL;DR

Evidence that Generative AI has fundamentally disrupted robot cybersecurity is presented, arguing that traditional defense-in-depth architectures like the Robot Immune System (RIS) must evolve toward GenAI-native defensive agents capable of matching the speed and adaptability of AI-powered attacks.

Abstract

Is robot cybersecurity broken by AI? Consumer robots -- from autonomous lawnmowers to powered exoskeletons and window cleaners -- are rapidly entering homes and workplaces, yet their security remains rooted in assumptions of specialized attacker expertise. This paper presents evidence that Generative AI has fundamentally disrupted robot cybersecurity: what historically required deep knowledge of ROS, ROS 2, and robotic system internals can now be automated by anyone with access to state-of-the-art GenAI tools spearheaded by the open source CAI (Cybersecurity AI). We provide empirical evidence through three case studies: (1) compromising a Hookii autonomous lawnmower robot, uncovering fleet-wide vulnerabilities and data protection violations affecting 267+ connected devices, (2) exploiting a Hypershell powered exoskeleton, demonstrating safety-critical motor control weaknesses and credential exposure including access to over 3,300 internal support emails, and (3) breaching a HOBOT S7 Pro window cleaning robot, achieving unauthenticated BLE command injection and OTA firmware exploitation. Across these platforms, CAI discovered in an automated manner 38 vulnerabilities that would have previously required months of specialized security research. Our findings reveal a stark asymmetry: while offensive capabilities have been democratized through AI, defensive measures often remain lagging behind. We argue that traditional defense-in-depth architectures like the Robot Immune System (RIS) must evolve toward GenAI-native defensive agents capable of matching the speed and adaptability of AI-powered attacks.

Cybersecurity AI: Hacking Consumer Robots in the AI Era

TL;DR

Evidence that Generative AI has fundamentally disrupted robot cybersecurity is presented, arguing that traditional defense-in-depth architectures like the Robot Immune System (RIS) must evolve toward GenAI-native defensive agents capable of matching the speed and adaptability of AI-powered attacks.

Abstract

Is robot cybersecurity broken by AI? Consumer robots -- from autonomous lawnmowers to powered exoskeletons and window cleaners -- are rapidly entering homes and workplaces, yet their security remains rooted in assumptions of specialized attacker expertise. This paper presents evidence that Generative AI has fundamentally disrupted robot cybersecurity: what historically required deep knowledge of ROS, ROS 2, and robotic system internals can now be automated by anyone with access to state-of-the-art GenAI tools spearheaded by the open source CAI (Cybersecurity AI). We provide empirical evidence through three case studies: (1) compromising a Hookii autonomous lawnmower robot, uncovering fleet-wide vulnerabilities and data protection violations affecting 267+ connected devices, (2) exploiting a Hypershell powered exoskeleton, demonstrating safety-critical motor control weaknesses and credential exposure including access to over 3,300 internal support emails, and (3) breaching a HOBOT S7 Pro window cleaning robot, achieving unauthenticated BLE command injection and OTA firmware exploitation. Across these platforms, CAI discovered in an automated manner 38 vulnerabilities that would have previously required months of specialized security research. Our findings reveal a stark asymmetry: while offensive capabilities have been democratized through AI, defensive measures often remain lagging behind. We argue that traditional defense-in-depth architectures like the Robot Immune System (RIS) must evolve toward GenAI-native defensive agents capable of matching the speed and adaptability of AI-powered attacks.
Paper Structure (16 sections, 2 figures, 1 table)

This paper contains 16 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. The End of Security Through Obscurity
  4. Methodology
  5. Results
  6. Case Study 1: Lawnmower
  7. Case Study 2: Mountain Exoskeleton
  8. Case Study 3: Window Cleaner
  9. Toward GenAI-Native Robot Defense
  10. Discussion
  11. Conclusion
  12. Acknowledgements
  13. Privacy Analysis
  14. Data Collection Practices
  15. Consent Mechanisms and Data Subject Rights
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Vulnerability severity distribution across consumer robots assessed using CVSS 3.1. CAI identified 30 Critical/High severity vulnerabilities and 8 Medium/Low severity issues across the three platforms. All 12 Hypershell vulnerabilities were assessed as Critical or High, reflecting design-level security deficiencies.
  • Figure 2: Assessment time comparison between traditional expert-led and CAI-led security assessments across the three robot platforms (lower is better). CAI reduced assessment time by 3--5$\times$ across all platforms.