arXiv subjects

David Schmidt

Publications and source records attributed to David Schmidt.

17 recordsLinked to original sources

arXiv2026

Send and Pretend: Exploiting Transcript Consistency Issues in End-to-End Encrypted Group Chats

End-to-end encrypted (E2EE) messaging apps are widely praised for their security and thus also used for sensitive coordination in group chats (e.g., by political decision makers). After Threema and WhatsApp, also Signal and iMessage have recently introduced polls to aid agreement processes in groups. This implicitly sets the expectation that all participants see the same outcome and thus have the same view of the conversation. This property is commonly referred to as transcript consistency (TC). In this work, we demonstrate that today's major E2EE messengers do not guarantee any form of TC for group chats, allowing a malicious group member to selectively omit, reorder, or present altered content to different recipients without triggering warnings in their user interface. We systematically investigate the extent of the problem under a malicious-participant threat model that targets the integrity of the shared transcript, or inconsistent delivery across a user's linked devices. We identify multiple equivocation vectors that range from protocol fallback paths to deliberate use of pairwise delivery channels within groups. We demonstrate concrete exploitation scenarios such as social engineering, evading moderation, and, in particular, rigging polls. Beyond these cross-service design issues, we also uncover implementation-specific behaviors with privacy implications (e.g., device OS fingerprinting). Finally, we contextualize our findings within prior transcript-consistency research and outline practical low-overhead mitigations and UI signaling strategies that can be integrated into state-of-the-art E2EE group protocols.

cs.CR↗

arXiv2026

Context Matters: Repository-Aware Security Analysis of the Agent Skill Ecosystem

Agent skills extend local AI agents, such as Claude Code and OpenClaw, with additional functionality. Their growing popularity has led to dedicated marketplaces resembling mobile app stores, as well as automated scanners that assess whether skills are benign or malicious. However, scanner reports from individual marketplaces classify up to 46.8% of skills as malicious, raising concerns about false positives. We present the largest empirical security analysis of the AI agent skill ecosystem to date. We collect 238,180 unique skills from three major distribution platforms and GitHub, and analyze their contents, behavior, and repository context. Unlike existing scanner-based assessments, which evaluate skills largely in isolation, our repository-aware analysis checks whether a flagged skill is consistent with its surrounding GitHub project. This context substantially reduces the number of suspicious skills: only 0.52% remain suspicious after repository-aware analysis. Our results show that existing scanners can substantially overestimate maliciousness when repository context is ignored. At the same time, we identify previously undocumented real-world attack vectors, including the hijacking of skills hosted in abandoned GitHub repositories. Overall, our findings provide a more robust view of the agent-skill ecosystem's current risk surface and highlight the need for context-aware security evaluation.

cs.CR↗

arXiv2026

Supply Chain Insecurity: Exposing Vulnerabilities in iOS Dependency Management Systems

Dependency management systems are a critical component in software development, enabling projects to incorporate existing functionality efficiently. However, misconfigurations and malicious actors in these systems pose severe security risks, leading to supply chain attacks. Despite the widespread use of smartphone apps, the security of dependency management systems in the iOS software supply chain has received limited attention. In this paper, we focus on CocoaPods, one of the most widely used dependency management systems for iOS app development, but also examine the security of Carthage and Swift Package Manager (SwiftPM). We demonstrate that iOS apps expose internal package names and versions. Attackers can exploit this leakage to register previously unclaimed dependencies in CocoaPods, enabling remote code execution (RCE) on developer machines and build servers. Additionally, we show that attackers can compromise dependencies by reclaiming abandoned domains and GitHub URLs. Analyzing a dataset of 9,212 apps, we quantify how many apps are susceptible to these vulnerabilities. Further, we inspect the use of vulnerable dependencies within public GitHub repositories. Our findings reveal that popular apps disclose internal dependency information, enabling dependency confusion attacks. Furthermore, we show that hijacking a single CocoaPod library through an abandoned domain could compromise 63 iOS apps, affecting millions of users. Finally, we compare iOS dependency management systems with Cargo, Go modules, Maven, npm, and pip to discuss mitigation strategies for the identified threats.

cs.CR↗

arXiv2025

Phase matching in Vector Beam Driven High Harmonic Generation with 3D-printed Gas Cells

We present experimental results of high harmonic generation(HHG) driven by a 1300 nm beam in three different polarization states: linear, radial, and azimuthal. We found that the optimal pressure for phasematching was roughly twice as high for the vector beam drivers than the linear driver. We attribute this difference in pressure primarily to the Gouy phase, which differs by a factor of two between the linear and vector polarization states. We demonstrate a target for HHG that produces a uniform pressure profile in the interaction region that is nearly identical to the backing pressure, and preserves the mode of the driving beam. We provide characterization and validation of this technique through flow simulations and experimental measurements.

physics.optics↗

arXiv2025

AugerPrime: Status and first results

With the knowledge and statistical precision derived from two decades of measurement, the Pierre Auger Observatory has significantly deepened our understanding of ultra-high-energy cosmic rays while unearthing an increasingly complex astrophysical landscape and exposing tensions with hadronic interaction models. The field now demands the mass of individual cosmic-ray primaries as an observable with an exposure that only the 3000-square-kilometer surface array of the Observatory can provide. Access to the primary mass hinges on the disentanglement of the electromagnetic and muonic components of extensive air showers. To achieve this, scintillator and radio detectors have been installed atop each existing water-Cherenkov detector of the surface array, whose dynamic range has also been enhanced through the installation of small-area PMTs. Additionally, the timing and signal resolution of all detector stations have been improved through upgraded station electronics, and underground muon counters have been installed in a region of the array with denser spacing. As the commissioning of the final components of AugerPrime reaches its conclusion and the enhanced array comes fully online, we present the realization of its design, its performance, and the first results from this now multi-hybrid observatory.

astro-ph.IM↗

arXiv2024

Neutron Production in Simulations of Extensive Air Showers

Although the electromagnetic and muonic components of extensive air showers have been studied in great detail, no comprehensive simulation study of the neutron component is available. This is related to the complexity of neutron transport processes that is typically not treated in standard simulation tools. In this work we use the Monte Carlo simulation package Fluka to study the production and the transport of neutrons in extensive air showers over the full range of neutron energies, extending down to thermal neutrons. The importance of different neutron production mechanisms and their impact on predicted neutron distributions in energy, lateral distance, atmospheric depth, and arrival time are discussed. In addition, the dependencies of the predictions on the properties of the primary particle are studied. The results are compared to the equivalent distributions of muons, which serve as reference.

hep-ph↗

arXiv2024

A Model of the Response of Surface Detectors to Extensive Air Showers Based on Shower Universality

We present a full model of surface-detector responses to extensive air showers. The model is motivated by the principles of air-shower universality and can be applied to different types of surface detectors. Here we describe a parametrization for both water-Cerenkov detectors and scintillator surface detectors, as for instance employed by the upgraded detector array of the Pierre Auger Observatory. Using surface detector data, the model can be used to reconstruct with reasonable precision shower observables such as the depth of the shower maximum $X_\text{max}$ and the number of muons $R_\mu$.

hep-ph↗

arXiv2024

Attosecond vortex pulse trains

The landscape of ultrafast structured light pulses has recently evolved driven by the capability of high-order harmonic generation (HHG) to up-convert orbital angular momentum (OAM) from the infrared to the extreme-ultraviolet (EUV) spectral regime. Accordingly, HHG has been proven to produce EUV vortex pulses at the femtosecond timescale. Here we demonstrate the generation of attosecond vortex pulse trains, i.e. a succession of attosecond pulses with a helical wavefront, resulting from the synthesis of a comb of EUV high-order harmonics with the same OAM. By driving HHG with a polarization tilt-angle fork grating, two spatially separated circularly polarized high-order harmonic beams with order-independent OAM are created. Our work opens the route towards attosecond-resolved OAM light-matter interactions.

physics.optics↗

arXiv2023

Airborne Sound Analysis for the Detection of Bearing Faults in Railway Vehicles with Real-World Data

In this paper, we address the challenging problem of detecting bearing faults in railway vehicles by analyzing acoustic signals recorded during regular operation. For this, we introduce Mel Frequency Cepstral Coefficients (MFCCs) as features, which form the input to a simple Multi-Layer Perceptron classifier. The proposed method is evaluated with real-world data that was obtained for state-of-the-art commuter railway vehicles in a measurement campaign. The experiments show that with the chosen MFCC features bearing faults can be reliably detected even for bearing damages that were not included in training.

eess.AS↗

arXiv2022

Emergency Management and Recovery of Luna Classic

In early May 2022, the Terra ecosystem collapsed after the algorithmic stablecoin failed to maintain its peg. Emergency measures were taken by Terraform Labs (TFL) in an attempt to protect Luna and UST, but then were abruptly abandoned by TFL for Luna 2.0 several days later. At this time, the Luna Classic blockchain has been left crippled and in limbo for the last two months. In the face of impossible odds, the Luna Classic community has self organized and rallied to build and restore the blockchain. This technical document outlines the steps we, the community, have taken towards the emergency management of the Luna Classic blockchain in the weeks after the UST depeg. We outline precisely what would be implemented on-chain to mitigate the concerns of affected stakeholders, and build trust for external partners, exchanges, and third-party developers. For the Luna Classic community, validators, and developers, this outlines concrete steps on how passed governance can and will be achieved. We openly audit our own code and welcome any feedback for improvement. Let us move forward together as the true community blockchain.

cs.CR↗

arXiv2021

Machine-learning accelerated turbulence modelling of transient flashing jets

Modelling the sudden depressurisation of superheated liquids through nozzles is a challenge because the pressure drop causes rapid flash boiling of the liquid. The resulting jet usually demonstrates a wide range of structures, including ligaments and droplets, due to both mechanical and thermodynamic effects. As the simulation comprises increasingly numerous phenomena, the computational cost begins to increase. One way to moderate the additional cost is to use machine learning surrogacy for specific elements of the calculations. The present study presents a machine learning-assisted computational fluid dynamics approach for simulating the atomisation of flashing liquids accounting for distinct stages, from primary atomisation to secondary break-up to small droplets using the ${\Sigma}$-Y model coupled with the homogeneous relaxation model. Notably, the model for the thermodynamic non-equilibrium (HRM) and ${\Sigma}$-Y are coupled, for the first time, with a deep neural network that simulates the turbulence quantities, which are then used in the prediction of superheated liquid jet atomisation. The data-driven component of the method is used for turbulence modelling, avoiding the solution of the two-equation turbulence model typically used for Reynolds-averaged Navier-Stokes simulations for these problems. Both the accuracy and speed of the hybrid approach are evaluated, demonstrating adequate accuracy and at least 25% faster computational fluid dynamics simulations than the traditional approach. This acceleration suggests that perhaps additional components of the calculation could be replaced for even further benefit.

physics.flu-dyn↗

arXiv2021

Network Compression for Machine-Learnt Fluid Simulations

Multi-scale, multi-fidelity numerical simulations form the pillar of scientific applications related to numerically modeling fluids. However, simulating the fluid behavior characterized by the non-linear Navier Stokes equations are often times computational expensive. Physics informed machine learning methods is a viable alternative and as such has seen great interest in the community [refer to Kutz (2017); Brunton et al. (2020); Duraisamy et al. (2019) for a detailed review on this topic]. For full physics emulators, the cost of network inference is often trivial. However, in the current paradigm of data-driven fluid mechanics models are built as surrogates for complex sub-processes. These models are then used in conjunction to the Navier Stokes solvers, which makes ML model inference an important factor in the terms of algorithmic latency. With the ever growing size of networks, and often times overparameterization, exploring effective network compression techniques becomes not only relevant but critical for engineering systems design. In this study, we explore the applicability of pruning and quantization (FP32 to int8) methods for one such application relevant to modeling fluid turbulence. Post-compression, we demonstrate the improvement in the accuracy of network predictions and build intuition in the process by comparing the compressed to the original network state.

physics.comp-ph↗

arXiv2021

Probing hadronic interactions with measurements at ultra-high energies with the Pierre Auger Observatory

The characteristics of an extensive air shower derive from both the mass of the primary ultra-high-energy cosmic ray that seeds its development and the properties of the hadronic interactions that feed it. With its hybrid detector design, the Pierre Auger Observatory measures both the longitudinal development of showers in the atmosphere and the lateral distribution of particles arriving at the ground, from which a number of parameters are calculated and compared with predictions from current hadronic interaction models tuned to LHC data. At present, a tension exists concerning the production of muons, in that the measured abundance exceeds all predictions. This discrepancy, measured up to center-of-mass energies of $\sim$ 140 TeV, is irresolvable through mass composition arguments, constrained by measurements of the depth of the electromagnetic-shower maximum. Here, we discuss a compilation of hadronically-sensitive shower observables and their comparisons with model predictions and conclude with a brief discussion of what measurements with the new detectors of the AugerPrime upgrade will bring to the table.

astro-ph.HE↗

arXiv2020

Self-consistent approach for measuring the energy spectra and composition of cosmic rays and determining the properties of hadronic interactions at high energy

Air showers, produced by the interaction of energetic cosmic rays with the atmosphere, are an excellent alternative to study particle physics at energies beyond any human-made particle accelerator. For that, it is necessary to identify first the mass composition of the primary cosmic ray (and its energy). None of the existing high energy interaction models have been able to reproduce coherently all air shower observables over the entire energy and zenith angle phase space. This is despite having tried all possible combinations for the cosmic ray mass composition. This proposal outlines a self-consistent strategy to study high energy particle interactions and identify the energy spectra and mass composition of cosmic rays. This strategy involves the participation of different particle accelerators and astrophysics experiments. This is important to cover the entire cosmic ray energy range and a larger phase-space of shower observables to probe the high energy interaction models.

astro-ph.HE↗

arXiv2019

Scintillator Surface Detector simulations for AugerPrime

Knowledge of the mass composition of ultra-high-energy cosmic rays is understood to be a salient component in answering the open questions in the field. The AugerPrime upgrade of the Pierre Auger Observatory aims to enhance its surface detector with the hardware necessary to reconstruct primary mass for individual events. This involves placing a scintillation-based detector with an active area of $3.8 \,\mathrm{m}^2$ on top of each existing water-Cherenkov detector in its surface detector array. Here, we present the methods for simulating this Scintillator Surface Detector. These simulations have and will continue to aid in the interpretation of measurements with AugerPrime as well as the development and improvement of event reconstruction algorithms including primary mass.

astro-ph.IM↗

arXiv2018

Origins of Extragalactic Cosmic Ray Nuclei by Contracting Alignment Patterns induced in the Galactic Magnetic Field

We present a novel approach to search for origins of ultra-high energy cosmic rays. These particles are likely nuclei that initiate extensive air showers in the Earth's atmosphere. In large-area observatories, the particle arrival directions are measured together with their energies and the atmospheric depth at which their showers maximize. The depths provide rough measures of the nuclear charges. In a simultaneous fit to all observed cosmic rays we use the galactic magnetic field as a mass spectrometer and adapt the nuclear charges such that their extragalactic arrival directions are concentrated in as few directions as possible. Using different simulated examples we show that, with the measurements on Earth, reconstruction of extragalactic source directions is possible. In particular, we show in an astrophysical scenario that source directions can be reconstructed even within a substantial isotropic background.

astro-ph.IM↗

arXiv2018

Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks

We use adversarial network architectures together with the Wasserstein distance to generate or refine simulated detector data. The data reflect two-dimensional projections of spatially distributed signal patterns with a broad spectrum of applications. As an example, we use an observatory to detect cosmic ray-induced air showers with a ground-based array of particle detectors. First we investigate a method of generating detector patterns with variable signal strengths while constraining the primary particle energy. We then present a technique to refine simulated time traces of detectors to match corresponding data distributions. With this method we demonstrate that training a deep network with refined data-like signal traces leads to a more precise energy reconstruction of data events compared to training with the originally simulated traces.

astro-ph.IM↗