  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.
