[Paper Review] Securing Autonomous Service Robots through Fuzzing, Detection, and Mitigation

Chundong Wang, Yee Ching Tok+3 more|arXiv (Cornell University)|Mar 12, 2020

Smart Grid Security and Resilience41 references4 citations

TL;DR

This paper proposes RoboFuzz, a directed fuzzing framework that systematically tests autonomous service robots by injecting rational but malicious sensor values to expose vulnerabilities in their control programs. It further introduces Shade and Remit—detection and mitigation algorithms that use historical obstacle records to identify spoofed sensor data and maintain robot functionality with only 4.1% loss in work efficacy, achieving 93.3% attack success rate in testing.

ABSTRACT

Autonomous service robots share social spaces with humans, usually working together for domestic or professional tasks. Cyber security breaches in such robots undermine the trust between humans and robots. In this paper, we investigate how to apprehend and inflict security threats at the design and implementation stage of a movable autonomous service robot. To this end, we leverage the idea of directed fuzzing and design RoboFuzz that systematically tests an autonomous service robot in line with the robot's states and the surrounding environment. The methodology of RoboFuzz is to study critical environmental parameters affecting the robot's state transitions and subject the robot control program with rational but harmful sensor values so as to compromise the robot. Furthermore, we develop detection and mitigation algorithms to counteract the impact of RoboFuzz. The difficulties mainly lie in the trade-off among limited computation resources, timely detection and the retention of work efficiency in mitigation. In particular, we propose detection and mitigation methods that take advantage of historical records of obstacles to detect inconsistent obstacle appearances regarding untrustworthy sensor values and navigate the movable robot to continue moving so as to carry on a planned task. By doing so, we manage to maintain a low cost for detection and mitigation but also retain the robot's work efficacy. We have prototyped the bundle of RoboFuzz, detection and mitigation algorithms in a real-world movable robot. Experimental results confirm that RoboFuzz makes a success rate of up to 93.3% in imposing concrete threats to the robot while the overall loss of work efficacy is merely 4.1% at the mitigation mode.

Motivation & Objective

• To address the critical security gap in movable autonomous service robots, which are vulnerable to sensor-based cyberattacks due to limited computational resources and mobility.
• To develop a practical, on-device detection and mitigation mechanism that operates within tight resource constraints and preserves task continuity.
• To systematically identify vulnerabilities in robot control programs by simulating realistic yet harmful sensor inputs through directed fuzzing.
• To enhance trust in autonomous robots by securing them at the design and implementation stage, especially in human-robot shared environments.
• To enable real-time attack detection and recovery without relying on cloud-based attestation or remote computation.

Proposed method

• Design and implement RoboFuzz, a directed fuzzing framework that generates harmful sensor values based on the robot’s current state and environmental context to trigger control program flaws.
• Use Radamsa, a state-of-the-art fuzzer, to generate volatile and malicious sensor inputs that simulate realistic but deceptive environmental readings.
• Develop Shade, a detection algorithm that compares real-time obstacle appearances with historical records to identify inconsistencies indicating spoofed sensor data.
• Design Remit, a mitigation algorithm that navigates the robot to continue task execution using safe trajectories when spoofed sensor values are detected.
• Integrate Shade and Remit into a real-world cleaning robot prototype to validate performance under resource-constrained, on-device operation.
• Leverage historical obstacle data to maintain detection accuracy and navigation efficiency without requiring external cloud support.

Experimental results

Research questions

• RQ1 How can sensor-based cyberattacks be systematically generated to compromise the control logic of movable autonomous service robots?
• RQ2 What on-device detection mechanism can effectively identify spoofed sensor values using only local historical data?
• RQ3 How can a mitigation strategy maintain high task efficacy while operating under strict computational and energy constraints?
• RQ4 To what extent can directed fuzzing outperform general-purpose fuzzing in compromising robot control programs?
• RQ5 Can detection and mitigation mechanisms preserve robot functionality with minimal performance degradation in real-world deployment?

Key findings

• RoboFuzz achieved a 93.3% success rate in compromising the robot’s control program by injecting rational but harmful sensor values during critical state transitions.
• The detection algorithm Shade successfully identified inconsistent obstacle appearances caused by spoofed sensor inputs using only local historical data.
• The mitigation algorithm Remit maintained 95.9% of the robot’s original work efficacy, resulting in only 4.1% loss despite active attacks.
• The proposed detection and mitigation framework operates efficiently within the limited computational and battery resources of a real-world autonomous service robot.
• The integration of RoboFuzz, Shade, and Remit into a physical cleaning robot prototype demonstrated practical feasibility and high resilience in real-world testing.
• The approach outperforms traditional fuzzing tools by focusing on context-aware, state-dependent sensor input mutation, increasing exploitability and realism.