The paper adapts kill chain methodology from classical IT security to quantum machine learning, organizing published QML attacks into a five-stage lifecycle with attacker roles, capabilities, and defenses.
Shuttle-Exploiting Attacks and Their Defenses in Trapped-Ion Quantum Computers
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abstract
Trapped-ion (TI) quantum bits are a front-runner technology for quantum computing. TI systems with multiple interconnected traps can overcome the hardware connectivity issue inherent in superconducting qubits and can solve practical problems at scale. With a sufficient number of qubits on the horizon, the multi-programming model for Quantum Computers (QC) has been proposed where multiple users share the same QC for their computing. Multi-programming is enticing for quantum cloud providers as it can maximize device utilization, throughput, and profit for clouds. Users can also benefit from the short wait queue. However, shared access to quantum computers can create new security issues. This paper presents one such vulnerability in shared TI systems that require shuttle operations for communication among traps. Repeated shuttle operations increase quantum bit energy and degrade the reliability of computations (fidelity). We show adversarial program design approaches requiring numerous shuttles. We propose a random and systematic methodology for adversary program generation. Our analysis shows shuttle-exploiting attacks can substantially degrade the fidelities of victim programs by 2X to 63X. Finally, we present several countermeasures such as adopting a hybrid initial mapping policy, padding victim programs with dummy qubits, and capping maximum shuttles.
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Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security
The paper adapts kill chain methodology from classical IT security to quantum machine learning, organizing published QML attacks into a five-stage lifecycle with attacker roles, capabilities, and defenses.