The researchers demonstrated a complete multi-stage attack on quantum neural networks, going beyond isolated vulnerability studies to present a realistic threat scenario. A team of Cedric Brügmann, Daniel Herr, Daniel Ohl de Mello, and colleagues successfully combined reconnaissance, crosstalk characterization, adversarial example generation, and physical attacks all on a trapped ion quantum computer. This end-to-end “kill chain” highlights how an attacker can leverage information gathered during initial reconnaissance to refine subsequent attack stages, an important consideration for quantum provider-as-a-service and multi-tenant environments. As the authors note, this research is based on an extensive review of the literature to align existing quantum machine learning attack vectors with the MITER ATLAS framework, revealing the interconnectedness of hardware weaknesses and data manipulation techniques. Corresponding experiments on superconducting hardware are also reported in the appendix.
Trapped ion hardware for quantum neural network attacks
The researchers detailed how the information collected during the initial reconnaissance phase directly improves the effectiveness of the subsequent attack phase. This was very different from previous individual vulnerability investigations. This multi-layered approach simulates realistic threat scenarios that are particularly relevant to quantum-as-a-service (QaaS) environments where multiple users share hardware resources. The research team specifically targeted a trapped ion quantum computer, successfully carried out a “kill chain” attack, and reported the corresponding superconducting hardware experiments in an appendix. The focus on trapped ion systems highlights certain hardware vulnerabilities, as side-channel attacks exploiting power traces and timing have been demonstrated in superconducting devices. However, the researchers extended this work to another physical platform, demonstrating the broader applicability of these techniques. The authors discuss how this study builds on an extensive review of the literature and emphasizes the end-to-end nature of the demonstration.
Importantly, the attack vectors used operate within the limits of current noisy intermediate-scale quantum (NISQ) devices, meaning they do not rely on the existence of fault-tolerant quantum computers. This makes the demonstrated threat immediately relevant to near-term quantum machine learning deployments. This framework facilitates understanding of interconnected vulnerabilities and enables the design of proactive, integrated, and layered defense strategies against increasingly sophisticated quantum threats.
Side-channel attacks targeting quantum circuit reverse engineering
While current efforts to secure quantum machine learning (QML) often address individual vulnerabilities in isolation, recent research has demonstrated a more holistic threat model. Beyond investigating a single attack vector, researchers are working to simulate a complete multi-stage “kill chain” for quantum neural networks. This multi-layered approach, detailed in a paper by Cedric Brügmann et al., more accurately reflects how a determined adversary operates, especially in a cloud-based quantum computing environment. The team specifically focused on demonstrating this attack sequence against a trapped ion system, and reports the corresponding experiments performed on superconducting hardware in the appendix. The main difference from previous studies is that we utilize the information collected during the reconnaissance phase to enhance the subsequent attack phase. This means that rather than simply attempting a single compromise, attackers actively learn about the target system, its circuitry, and resource usage to refine their techniques.
This reconnaissance can leverage side-channel attacks such as analyzing power traces and timing variations to reverse engineer the quantum circuit itself. The study is based on an extensive review of the literature and places the study within the broader context of quantum security research.
Their recent research details how reconnaissance, which gathers information about target systems, is not just preparation, but essential to amplifying subsequent attack phases. This is in contrast to previous studies where each attack vector was typically evaluated separately. The framework exposes the interdependencies between different threat classes, ranging from hardware-level weaknesses such as side-channel leaks and crosstalk-induced failures, data and algorithm manipulations such as poisoning and circuit backdoors, and privacy-focused attacks such as model extraction and training data inference. An important finding is the possibility of linked multi-stage attacks. In this case, the attacker uses side-channel attacks to perform reconnaissance, learn as much as possible about the victim’s machine learning model, and use this information to fine-tune the attack, for example based on noise injection, to attack the model. This is particularly relevant for Quantum-as-a-Service (QaaS) providers and multi-tenant quantum systems. By systematically evaluating these interdependencies, the team hopes to enable the design of proactive, integrated, defense-in-depth strategies that address the evolving threat landscape.
Researchers have now gone beyond simply identifying vulnerabilities to demonstrating a complete multi-stage attack on quantum machine learning systems, revealing a realistic threat landscape for quantum devices in the near term. This work details how attackers can exploit the physical interactions between qubits, particularly crosstalk, to inject noise and manipulate computations, going beyond theoretical risks to demonstrate practical attack vectors. The key innovation lies in the interconnectivity of the attack phase. Unlike previous studies, this study highlights that the information obtained during reconnaissance can be used to reduce uncertainty in subsequent stages, allowing targeted operations, especially related to cloud or multi-tenant environments. This multi-layered approach simulates realistic scenarios where attackers do not act in isolation, but instead build on collected information. The research specifically emphasizes how crosstalk, a phenomenon in which operations on one qubit unintentionally affect other qubits, can be exploited. Similar to previous findings in superconducting architectures, this work demonstrated that intentional noise injection through crosstalk can disrupt quantum calculations, but this time applied to a trapped ion system.
Beyond individual vulnerability studies, researchers are now demonstrating a complete multi-stage attack chain against quantum systems that mirrors the sophisticated campaigns seen in classical cybersecurity. The team’s research goes beyond theoretical risks by demonstrating realistic attack scenarios that can be applied to quantum devices in the near future.
The researchers detail how attackers progress through a structured “kill chain” mirroring established cybersecurity frameworks such as MITER ATLAS to compromise quantum systems. This approach goes beyond individual vulnerability research by explicitly mapping attack vectors into a series of phases, from initial information gathering to impactful disruption. The team developed a taxonomy of quantum machine learning attack vectors and aligned them to each stage of the MITER ATLAS framework. The framework is built on an extensive review of the literature and clarifies the interdependencies between different threat classes, ranging from hardware-level weaknesses, data and algorithm manipulation, and privacy-focused attacks. Experiments on superconducting hardware are reported in the appendix, revealing vulnerabilities across a variety of physical platforms. The team’s framework recognizes that attacks unfold as structured campaigns rather than individual events, making it easier to understand interconnected vulnerabilities. As all attack vectors evaluated operate within the constraints of current noisy medium-sized quantum devices, a nuanced understanding of interrelated vulnerabilities is critical to building robust defenses against increasingly sophisticated quantum threats.
Reconnaissance via side channels to refine attacks
Recent research shows a shift beyond individual vulnerability studies toward understanding quantum machine learning (QML) attacks as comprehensive, multi-stage campaigns. A key element of these evolving threats is the use of reconnaissance, or initial intelligence gathering, to increase the effectiveness of subsequent attack phases. This is a tactic that has not received much attention in quantum security research. Researchers are now showing how attackers can proactively leverage the intelligence they collect to carry out more targeted and successful operations, especially within cloud and multi-tenant quantum computing environments. This sophisticated approach goes beyond simply identifying potential weaknesses. It focuses on how attackers chain vulnerabilities together. The framework clarifies the interdependencies between different threat classes, ranging from hardware-level weaknesses, data and algorithm manipulation, and privacy-focused attacks. Side-channel attacks that exploit unintended information leaks from quantum systems are central to this reconnaissance phase.
While previous research has primarily focused on superconducting devices, this work extends the analysis to trapped ion platforms and investigates power tracing and timing-based approaches to reverse engineering quantum circuits. This detailed understanding of the target system enables more precise and effective attacks later in the kill chain, moving beyond common exploits to customized manipulation of quantum hardware and algorithms.
NISQ Device Constraints on Attack Vector Implementation
Quantum security research is increasingly focused on practical attacks that can be performed on current hardware, and recent work by Cedric Brügmann and colleagues demonstrates a comprehensive multi-stage attack against quantum neural networks running on trapped ion devices. In this layered approach, superconducting hardware experiments are reported in the appendix. This means attackers aren’t just looking for weaknesses. Use the intelligence you collect to adjust subsequent attack phases. This is especially important in cloud and multi-tenant environments where shared resources amplify risk. Their framework aligns existing attack vectors with the stages of a quantum-enabled kill chain, in the spirit of the classic ML MITER ATLAS framework. The framework clarifies the interdependencies between different threat classes, ranging from hardware-level weaknesses, data and algorithm manipulation, and privacy-focused attacks. By systematically evaluating these interdependencies, their approach facilitates the design of proactive, integrated defense-in-depth strategies.
One of the insights gained from this kill chain perspective is the possibility of linked multi-stage attacks. In this case, the attacker uses side-channel attacks to perform reconnaissance, learn as much as possible about the victim’s machine learning model, and use this information to fine-tune the attack, based on noise injection, for example, to attack the model. Importantly, all attack vectors evaluated operate within the constraints of noisy intermediate-scale quantum (NISQ) devices and do not rely on fault-tolerant assumptions, making them directly relevant for short-term quantum machine learning deployments.
The sophistication of quantum machine learning (QML) is requiring a shift in security thinking that moves beyond individual vulnerability research to encompass realistic, multi-stage attack scenarios. Researchers are now modeling these threats using a “kill chain” approach, originally developed for classical cybersecurity, to plan the complete attack lifecycle against quantum systems. This methodology recognizes that adversaries rarely act alone. Instead, they chain techniques together and leverage the information gained at each stage to refine subsequent attacks. This study emphasizes that reconnaissance is not just passive intelligence gathering. Proactively reduce uncertainty at later stages. The framework exposes the interdependencies between different threat classes, ranging from hardware-level weaknesses such as side-channel leaks and crosstalk-induced failures, data and algorithm manipulations such as poisoning and circuit backdoors, and privacy-focused attacks such as model extraction and training data inference. By systematically evaluating these interdependencies, their approach facilitates the design of proactive, integrated defense-in-depth strategies.
