A Conditional Timing Protection Level: Holdover-Limited Undetected Time Error Under GNSS Spoofing
- URL: http://arxiv.org/abs/2606.24210v1
- Date: Tue, 23 Jun 2026 06:52:58 GMT
- Title: A Conditional Timing Protection Level: Holdover-Limited Undetected Time Error Under GNSS Spoofing
- Abstract summary: A timing receiver under spoofing has no nominal-geometry fault for position-domain RAIM to bound.<n>We show a recorded over-the-air spoof from the public JammerTest 2024 campaign pulled a u-blox ZED-F9P by about 1.01 ms of served time.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: A GNSS timing receiver under spoofing has no nominal-geometry fault for position-domain RAIM to bound: the threat is a slow, common-mode pull of served clock time that the receiver's own time-accuracy flag need not reveal. We make three graded contributions. First, a field measurement: solving the receiver clock trajectory from raw L1 pseudoranges and broadcast ephemeris, we show a recorded over-the-air spoof from the public JammerTest 2024 campaign pulled a u-blox ZED-F9P by about 1.01 ms of served time while it reported at most 51 ns, a gap near 20,000x. Second, an impossibility: against an adversary free to choose the ramp rate, no finite unconditional bound on undetected time error exists under a single self-referential clock-aided monitor, because a ramp slow enough to keep the disciplined reference in lock-step is never alarmed while the error grows without limit, so any finite guarantee is conditional. Third, the conditional bound: the Timing Protection Level (TPL), a model-free monitor's static detectability floor plus the oscillator's coast over the detection latency, holds given detection by an independent cross-satellite consistency check a coherent spoofer does not drive in lock-step. Each term is a closed form over a primitive verified in the open Kshana simulator, so the sum is reproducible by hand. Calibrated on the recorded attack, the budget is 114 ns at one-second recovery and 458 ns at a 60-second coast, thousands of times below the 1.01 ms accepted; a clock-aided sequential test alone gives essentially no protection on this slow ramp (it alarms only near the ~1 ms capture), while the model-free monitor alarms during the ramp. We are explicit: the bound is calibrated, not field-validated; carries no integrity-risk budget; and is reported as a band at long coast. The simulator, bound, and calibration example are open source under AGPL-3.0.
Related papers
- Detecting Backdoors in Object Detection via Pre-NMS Prediction Distribution Shift [35.70747932100589]
DistScan is a backdoor detection framework based on a simple but previously unexploited observation.<n>It substantially outperforms existing methods, improving average detection accuracy over the best-performing applicable baseline by 27.32 percentage points.
arXiv Detail & Related papers (2026-08-19T16:38:39Z) - From Positionwise Confidence to Prefix Scheduling: Verifier Skipping in Speculative Decoding [41.41611499810411]
Speculative diffusion decoding (SDD) generates every position in a draft block in parallel with a discrete diffusion model.<n>This paper recognizes that this creates a new control handle: whether to invoke the verifier at all.
arXiv Detail & Related papers (2026-08-14T18:00:08Z) - Excitation-Supervised Closed-Loop Self-Calibration and Target Seeking for an Unknown-Pose Range-Bearing Relay [0.0]
This paper supplies the closed-loop layer that governs identifiability.<n>An excitation-supervised algorithm is used to supervise an excitation-reset controller.<n>A decay-rate sweep shows that supervision matters when a fixed schedule's decay outruns the unknown time-to-adequate-excitation.
arXiv Detail & Related papers (2026-08-12T19:00:07Z) - Rigid-Covert GNSS Spoofing of UAV Swarms: A Structural Blind Spot, Its Detection Limit, and Absolute-Anchor Defenses [0.0]
Cooperative UAV-s defenses commonly cross-check positions against measured interdrone geometry.<n>We show that this relative-geometry channel has a structural blind spot: a common, slowly varying translation (a rigidcovert shift, RigidShift) preserves all pairwise distances and is therefore unobservable to any relativewarm-only detector.<n>We present a centralized anchor-rooted recovery pipeline that reconstructs swarm geometry from inter-drone ranges.
arXiv Detail & Related papers (2026-08-07T07:15:55Z) - Amplify, Don't Create: Temporal Accumulation for Slow-Burn Prompt Injection [0.0]
Control-plane attacks against tool-using agents can instead distribute weak directives across a trajectory while keeping each event below threshold.<n>We test whether a proxy-side temporal accumulator recovers this slow-burn signal by reducing frozen per-event scores to peak and CUSUM persistence statistics.
arXiv Detail & Related papers (2026-06-17T23:39:38Z) - Bistable by Construction: Wall-Clock-Calibrated State Monitors Have No Moment-Detection Regime at Agent Cadence [0.0]
We report a State Saturation Trap: threshold-on-state triggers over a continuous affect engine become near-constant alarms on SWE-bench agents.<n>We correct the record (erratum, v2) and treat the flaw as an experiment.<n>We conclude that wall-clock-calibrated leaky-integrator monitors admit no regime in which they act as moment detectors on agent streams.
arXiv Detail & Related papers (2026-06-15T21:25:52Z) - SpecLoR: Spectral Lookahead Rectification for Motion-Coherent Text-to-Video Generation [39.71466927392354]
Flowmatching has enabled robust text-to-video generation via latent ODE.<n>SpectLoR significantly reduces physical artifacts and enhances motion coherence across multiple benchmarks with minimal computational overhead.
arXiv Detail & Related papers (2026-06-10T11:49:54Z) - The Distributed Detectability Band Against Marginal-Preserving Attacks [0.0]
AI-control monitors score individual agent actions to detect misbehavior.<n>Real harm can be distributed across many benign-looking steps, each individually below any per-step alarm.<n>We construct a marginal-preserving, correlation-encoded distributed-sabotage attack.
arXiv Detail & Related papers (2026-06-09T06:04:30Z) - Catching One in Five: LLM-as-Judge Blind Spots in Production Multi-Turn Transaction Agents [45.148328075418156]
We study a deployed multi-turn food-and-beverage ordering agent and measure how many genuine quality problems it catches.<n>Our blind-spot taxonomy shows the failure is structured, not random.<n>For production multi-turn agents, automated judging is a regression floor, not a substitute for human review.
arXiv Detail & Related papers (2026-06-09T02:11:01Z) - The Saturation Trap and the Subjectivity of Intervention Timing: Why Affect-Based Triggers and LLM Judges Fail to Time Interventions on Autonomous Agents [0.0]
Intervention timing is a low-reliability construct, making single-annotator F1 an unsuitable optimization target.<n>Our contribution is the joint mapping of this problem across human inter-rater reliability, four detector architectures, a cross-model LLM-judge sweep, and a reproduced saturation effect.
arXiv Detail & Related papers (2026-06-02T23:54:27Z) - Training-Free Object-Agnostic Jam Detection in Fulfillment Centers [35.18016233072556]
jam detection approaches rely on object detection models to identify objects, followed by tracking algorithms to monitor motion over time.<n>This pipeline requires thousands of manual annotations, consuming approximately two weeks of effort, and is limited to annotated object classes.<n>We present a training-free, object-agnostic jam detection method that eliminates the need for labeled data.
arXiv Detail & Related papers (2026-05-29T19:54:51Z) - Send a SCOUT First: Pre-hoc Reasoning for Adaptive Detector Allocation in Prompt-Injection Defense [37.10272384523361]
We develop a framework for predicting each detector's per-sample reliability and latency from how it behaved on similar past inputs.<n>We build SCOUT-450, a benchmark that captures the structurally complex, agent-facing injections that older prompt-injection sets under-represent.<n>On SCOUT-450, a safety-oriented operating point reduces attack-success rate by 46% and total wall-clock by 40% relative to an always-on GPT-4o judge.
arXiv Detail & Related papers (2026-05-29T04:49:20Z) - Detecting Object Tracking Failure via Sequential Hypothesis Testing [80.7891291021747]
Real-time online object tracking in videos constitutes a core task in computer vision.<n>We propose interpreting object tracking as a sequential hypothesis test, wherein evidence for or against tracking failures is gradually accumulated over time.<n>We propose both supervised and unsupervised variants by leveraging either ground-truth or solely internal tracking information.
arXiv Detail & Related papers (2026-02-13T14:57:15Z) - TARG: Training-Free Adaptive Retrieval Gating for Efficient RAG [46.122203287541005]
Training-free Adaptive Retrieval Gating (TARG) is a single-shot policy that decides when to retrieve using only a short, no-latency draft from the base model.<n>On NQ-Open, TriviaQA, and PopQA, TARG consistently shifts the accuracy-efficiency frontier.
arXiv Detail & Related papers (2025-11-12T23:09:52Z) - Uncertainty-aware Unsupervised Multi-Object Tracking [33.53331700312752]
unsupervised multi-object trackers are inferior to learning reliable feature embeddings.
Recent self-supervised techniques are adopted, whereas they failed to capture temporal relations.
This paper argues that though the uncertainty problem is inevitable, it is possible to leverage the uncertainty itself to improve the learned consistency in turn.
arXiv Detail & Related papers (2023-07-28T09:03:06Z) - 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) - Real-Time Anomaly Detection in Edge Streams [49.26098240310257]
We propose MIDAS, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges.
We further propose MIDAS-F, to solve the problem by which anomalies are incorporated into the algorithm's internal states.
Experiments show that MIDAS-F has significantly higher accuracy than MIDAS.
arXiv Detail & Related papers (2020-09-17T17:59:27Z)
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.