  Quantum Machine Learning (QML) has emerged as a promising intersection of
quantum computing and classical machine learning, anticipated to drive
breakthroughs in computational tasks. This paper discusses the question which
security concerns and strengths are connected to QML by means of a systematic
literature review. We categorize and review the security of QML models, their
vulnerabilities inherent to quantum architectures, and the mitigation
strategies proposed. The survey reveals that while QML possesses unique
strengths, it also introduces novel attack vectors not seen in classical
systems. We point out specific risks, such as cross-talk in superconducting
systems and forced repeated shuttle operations in ion-trap systems, which
threaten QML's reliability. However, approaches like adversarial training,
quantum noise exploitation, and quantum differential privacy have shown
potential in enhancing QML robustness. Our review discuss the need for
continued and rigorous research to ensure the secure deployment of QML in
real-world applications. This work serves as a foundational reference for
researchers and practitioners aiming to navigate the security aspects of QML.
