Recovery-Induced Erasure Attack on QKD Systems

• URL: http://arxiv.org/abs/2603.03217v1
• Date: Tue, 03 Mar 2026 18:08:56 GMT
• Title: Recovery-Induced Erasure Attack on QKD Systems
• Abstract summary: Detector dead time is typically treated as a fixed parameter in quantum key distribution (QKD) security analyses.<n>In this work, we demonstrate that this count-rate-dependent recovery nonlinearity constitutes a distinct attack primitive.
• Score: 0.9051352746190449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detector dead time is typically treated as a fixed parameter in quantum key distribution (QKD) security analyses. In practice, however, the effective recovery time of single-photon avalanche photodiodes (SPADs) depends on the incident count rate. In this work, we demonstrate that this count-rate-dependent recovery nonlinearity constitutes a distinct attack primitive. We experimentally characterize the dead time shift of a free-running SPAD under controlled broadband loading and observe a substantial increase in effective recovery time as the detected rate rises into the high photon count regime. We show that recovery-induced availability reduction can be modeled as an adversarial erasure channel and derive a conservative bound on the signal detection probability under loading. Unlike previously studied detector-control or efficiency mismatch attacks, the proposed mechanism does not rely on deterministic blinding or timing discrimination. Instead, count-rate-dependent recovery asymmetry induces basis-dependent suppression of detection probabilities ($p_\perp<p_\parallel$), converting mismatch-induced errors into loss. Particularly, we show in active-basis BBM92 systems, this effect reduces the observed quantum bit error rate (QBER) below the abort threshold while increasing erasure probability. Using experimentally measured detector recovery data, we quantify the parameter regime in which such stealth suppression is achievable. These results establish count-rate-dependent detector recovery as a security-relevant vulnerability and show that countermeasures designed for timing-based efficiency mismatch do not directly address recovery-induced erasure (RIE) attack. Our findings underscore the need to incorporate detector recovery dynamics explicitly into practical QKD security models.

Related papers

• Digital Twin Degradation: Detecting Cyber Physical Attacks via Temporal Inconsistencies [0.0]
  This paper proposes a detection framework that monitors temporal consistency between the physical system and a potentially degraded DT view.<n>An unsupervised density model characterizes normal consistency patterns, while a sequential change detection mechanism identifies sustained deviations indicative of attacks.<n>Results show that temporal inconsistency patterns enable reliable event-level attack detection with bounded false alarm rates and low detection latency.
  arXiv  Detail & Related papers  (2026-08-17T06:24:22Z)
• Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals [83.0127582612634]
  Near-boundary Rescue (NSR) is a minimal, plug-and-play modification that retains out-of-bound tokens to recover lost signals.<n>NSR substantially improves training stability and delivers consistent gains over strong baselines such as DAPO and GSPO.
  arXiv  Detail & Related papers  (2026-05-21T16:45:31Z)
• Active Bayesian Inference for Robust Control under Sensor False Data Injection Attacks [10.312968200748115]
  We present a framework for bridging the gap between sensor attack detection and recovery in cyber-physical systems.<n>We propose a threshold-based probing strategy and show its effectiveness via a simplified partially observable Markov decision process (POMDP) formulation.
  arXiv  Detail & Related papers  (2026-04-13T12:54:24Z)
• Fundamental Limits of Man-in-the-Middle Attack Detection in Model-Free Reinforcement Learning [2.9320341537404713]
  We consider the problem of learning-based man-in-the-middle (MITM) attacks in cyber-physical systems.<n>We refine the standard MDP attack model by allowing the reward function to depend on both the current and subsequent states.<n>We derive an optimal system-identification strategy for the adversary that minimizes detectable value deviations.
  arXiv  Detail & Related papers  (2026-03-29T09:18:00Z)
• Delayed Backdoor Attacks: Exploring the Temporal Dimension as a New Attack Surface in Pre-Trained Models [42.9538411462588]
  We introduce textittextbfDelayed Backdoor Attacks (DBA), a new class of threats in which activation is temporally decoupled from trigger exposure.<n>We implement a proof-of-concept prototype, termed underlineDelayed Backdoor Attacks Based on underlineDecay (DND)<n>DND embeds a lightweight, stateful logic module that postpones activation until a threshold is reached, producing a distinct latency phase followed by a controlled outbreak.
  arXiv  Detail & Related papers  (2026-03-12T13:57:11Z)
• Adversarial Learning Game for Intrusion Detection in Quantum Key Distribution [0.0]
  This paper presents a high-fidelity simulation framework for intrusion detection in decoy-state Quantum Key Distribution (QKD)<n>The framework is modeled as a minimax game between a learning-based defender and an adaptive adversary.<n>Under adaptive attack scenarios, the system preserves $82text--92%$ of the honest finite-key rate while discarding only approximately $1.2%$ of traffic.
  arXiv  Detail & Related papers  (2026-03-03T20:21:06Z)
• Limits of Residual-Based Detection for Physically Consistent False Data Injection [5.156484100374059]
  False data injection attacks (FDIAs) pose a persistent challenge to AC power system state estimation.<n>We present a data-driven constructive mechanism that incorporates the generic functional structure of AC power flow to generate physically consistent, manifold-constrained perturbations.
  arXiv  Detail & Related papers  (2026-02-10T07:29:45Z)
• Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspective [45.713195454899875]
  Diffusion models (DMs) have shown promise for Time-Series Data Imputation.<n>DMs' performance remains inconsistent in complex scenarios.<n>We propose a novel framework called SPIRIT (Semi-Proximal Transport Regularized time-series Imputation)
  arXiv  Detail & Related papers  (2026-02-01T12:11:57Z)
• Agentic Uncertainty Quantification [76.94013626702183]
  We propose a unified Dual-Process Agentic UQ (AUQ) framework that transforms verbalized uncertainty into active, bi-directional control signals.<n>Our architecture comprises two complementary mechanisms: System 1 (Uncertainty-Aware Memory, UAM), which implicitly propagates verbalized confidence and semantic explanations to prevent blind decision-making; and System 2 (Uncertainty-Aware Reflection, UAR), which utilizes these explanations as rational cues to trigger targeted inference-time resolution only when necessary.
  arXiv  Detail & Related papers  (2026-01-22T07:16:26Z)
• Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning [52.23804865017831]
  We address the problem of verifying closed-loop contraction in nonlinear control systems whose controller and contraction metric are both parameterized by neural networks.<n>We derive a tractable and scalable sufficient condition for closed-loop contractivity that reduces to checking that the dominant eigenvalue of a symmetric Metzler matrix is nonpositive.
  arXiv  Detail & Related papers  (2025-12-01T23:06:56Z)
• On the Adversarial Robustness of Learning-based Conformal Novelty Detection [10.58528988397402]
  We study the adversarial robustness of conformal novelty detection using AdaDetect.<n>Our results show that adversarial perturbations can significantly increase the FDR while maintaining high detection power.
  arXiv  Detail & Related papers  (2025-10-01T03:29:11Z)
• Phase error estimation for passive detection setups with imperfections and memory effects [0.8793721044482612]
  We develop a generic framework to bound the phase error rate for quantum key distribution protocols.<n>This framework can be used in proof techniques based on the entropic uncertainty relation or phase error correction.
  arXiv  Detail & Related papers  (2025-08-29T10:11:06Z)
• Convergence and Generalization of Anti-Regularization for Parametric Models [0.0]
  Anti-regularization introduces a reward term with a reversed sign into the loss function.<n>We formalize spectral safety conditions and trust-region constraints.<n>We design a lightweight safeguard that combines a projection operator with gradient clipping to guarantee stable intervention.
  arXiv  Detail & Related papers  (2025-08-24T15:34:17Z)
• Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable [70.77600345240867]
  A novel arbitrary-in-arbitrary-out (AIAO) strategy makes watermarks resilient to fine-tuning-based removal. Unlike the existing methods of designing a backdoor for the input/output space of diffusion models, in our method, we propose to embed the backdoor into the feature space of sampled subpaths. Our empirical studies on the MS-COCO, AFHQ, LSUN, CUB-200, and DreamBooth datasets confirm the robustness of AIAO.
  arXiv  Detail & Related papers  (2024-05-01T12:03:39Z)
• Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective [65.10019978876863]
  Diffusion-Based Purification (DBP) has emerged as an effective defense mechanism against adversarial attacks.<n>In this paper, we propose that the intrinsicity in the DBP process is the primary factor driving robustness.
  arXiv  Detail & Related papers  (2024-04-22T16:10:38Z)
• The Adversarial Implications of Variable-Time Inference [47.44631666803983]
  We present an approach that exploits a novel side channel in which the adversary simply measures the execution time of the algorithm used to post-process the predictions of the ML model under attack. We investigate leakage from the non-maximum suppression (NMS) algorithm, which plays a crucial role in the operation of object detectors. We demonstrate attacks against the YOLOv3 detector, leveraging the timing leakage to successfully evade object detection using adversarial examples, and perform dataset inference.
  arXiv  Detail & Related papers  (2023-09-05T11:53:17Z)
• Forecasting Particle Accelerator Interruptions Using Logistic LASSO Regression [62.997667081978825]
  Unforeseen particle accelerator interruptions, also known as interlocks, lead to abrupt operational changes despite being necessary safety measures. We propose a simple yet powerful binary classification model aiming to forecast such interruptions. The model is formulated as logistic regression penalized by at least absolute shrinkage and selection operator.
  arXiv  Detail & Related papers  (2023-03-15T23:11:30Z)
• Security proof of practical quantum key distribution with detection-efficiency mismatch [3.1988884923120313]
  We develop a method that allows to provide security proofs without the usual assumption. Our method can take the detection-efficiency mismatch into account without having to restrict the attack strategy of the adversary. Our method also shows that in the absence of efficiency mismatch in our detector model, the key rate increases if the loss due to detection inefficiency is assumed to be outside of the adversary's control.
  arXiv  Detail & Related papers  (2020-04-09T06:49:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.