Documented adversarial rendezvous and proximity operations (RPO) appear in the public TLE catalog, yet existing TLE-based maneuver detectors treat each satellite in isolation - mistaking routine station-keeping for adversarial behavior and missing RPO entirely when the target stays passive. We argue that the right unit is the satellite pair and the right inductive bias is the kinematic symmetry between approach and recede. We instantiate both with PairWatch, a pair-aware AnomalyTransformer whose attention prior is augmented with a sign-symmetric geometric similarity over relative-motion features, and release an open benchmark of 359 windows from public Space-Track data spanning 11 documented RPO events, 100 Clohessy-Wiltshire synthetic adversarial windows, and a five-class hard-negative set culminating in the TerraSAR-X / TanDEM-X helix formation. Under the deployment-relevant criterion of a single threshold separating real RPO from operational negatives - a necessary property for catalog-scale screening - PairWatch is the only model that succeeds, with the property holding in 100% of bootstrap resamples versus 0-19.5% for three neural baselines and under 0.1% for four physics-informed detectors. A per-source analysis exposes a shortcut-learning failure mode of single-number AUROC: simpler baselines attain higher overall AUROC by shortcutting a synthetic-vs-formation contrast with no operational meaning; removing this single cell flips the test ranking entirely (LSTM-AD), and these baselines fail the security-relevant task the benchmark was built to measure.
We present SPECTER, a post-compromise covert channel that uses host-side USB power control to exfiltrate information from air-gapped and network-segregated systems. A transmitter with administrative privileges encodes bits through USB power-management or device-state operations without placing the payload in USB data transfers. The resulting signals are recovered through three alternative receiver paths based on smartphone charging states, visible peripheral behavior, and near-field RF activity. Experiments on Windows 10 and Ubuntu 22.04 demonstrate the channel on commodity hardware, with charging-state signaling at 0.2-0.25 bit/s across Android and iOS devices and peripheral-state signaling at 1.0 bit/s using a USB mouse on Windows. RF reception is demonstrated using a software-defined radio with a loop antenna in direct contact with the USB cable. Channel availability and observable side effects depend on the host configuration, connected device, and receiver path. These results identify host-side USB power control as a mechanism for low-rate exfiltration of short secrets and motivate treating USB power-management and device-state events as security-relevant telemetry.
Hyeongsoo Lim, Jinyoung Kim, Eun Seo Seo Chung-Ang University, Min Ho Jang, Ji Won Yoon Chung-Ang University
Diffusion language models (DLMs) alleviate the inherent latency bottleneck of autoregressive (AR) large language models (LLMs), but their degraded generation quality limits practical applicability. Although knowledge distillation (KD) can be a promising direction for improving performance, we empirically find that naively applying conventional KD yields only marginal gains, or even degrades generation quality. Based on these observations, we propose a novel self-distillation framework for DLMs, namely SelFusion. To enable effective KD without an external teacher model, SelFusion performs two forward passes with different masking levels, defining the hard mode with a larger masking probability and the easy mode with a smaller masking probability. However, the easy mode is not always more accurate than the hard mode and can be overconfident on incorrect tokens. Thus, we introduce bidirectional KD between the two modes, which can dynamically determine the distillation direction based on token-level correctness. Experimental results on instruction-following tasks show that the proposed self-distillation substantially outperforms other KD methods with external LLM and DLM teachers. In many configurations, the student trained with SelFusion even surpasses the performance of the LLM teacher, providing a practical path toward improving DLM generation quality. Source code can be found at https://github.com/scai-research/SelFusion_official
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表