arXiv subjects

Moritz Schneider

Publications and source records attributed to Moritz Schneider.

At least 19 recordsLinked to original sources

arXiv2025

Systematic Prevention of On-Core Timing Channels by Full Temporal Partitioning

Microarchitectural timing channels enable unwanted information flow across security boundaries, violating fundamental security assumptions. They leverage timing variations of several state-holding microarchitectural components and have been demonstrated across instruction set architectures and hardware implementations. Analogously to memory protection, Ge et al. have proposed time protection for preventing information leakage via timing channels. They also showed that time protection calls for hardware support. This work leverages the open and extensible RISC-V instruction set architecture (ISA) to introduce the temporal fence instruction fence.t, which provides the required mechanisms by clearing vulnerable microarchitectural state and guaranteeing a history-independent context-switch latency. We propose and discuss three different implementations of fence.t and implement them on an experimental version of the seL4 microkernel and CVA6, an open-source, in-order, application class, 64-bit RISC-V core. We find that a complete, systematic, ISA-supported erasure of all non-architectural core components is the most effective implementation while featuring a low implementation effort, a minimal performance overhead of less than 1%, and negligible hardware costs.

cs.CR↗

arXiv2025

Information-Theoretic Policy Pre-Training with Empowerment

Empowerment, an information-theoretic measure of an agent's potential influence on its environment, has emerged as a powerful intrinsic motivation and exploration framework for reinforcement learning (RL). Besides for unsupervised RL and skill learning algorithms, the specific use of empowerment as a pre-training signal has received limited attention in the literature. We show that empowerment can be used as a pre-training signal for data-efficient downstream task adaptation. For this we extend the traditional notion of empowerment by introducing discounted empowerment, which balances the agent's control over the environment across short- and long-term horizons. Leveraging this formulation, we propose a novel pre-training paradigm that initializes policies to maximize discounted empowerment, enabling agents to acquire a robust understanding of environmental dynamics. We analyze empowerment-based pre-training for various existing RL algorithms and empirically demonstrate its potential as a general-purpose initialization strategy: empowerment-maximizing policies with long horizons are data-efficient and effective, leading to improved adaptability in downstream tasks. Our findings pave the way for future research to scale this framework to high-dimensional and complex tasks, further advancing the field of RL.

cs.AI↗

arXiv2025

Breaking Bad: How Compilers Break Constant-Time Implementations

The implementations of most hardened cryptographic libraries use defensive programming techniques for side-channel resistance. These techniques are usually specified as guidelines to developers on specific code patterns to use or avoid. Examples include performing arithmetic operations to choose between two variables instead of executing a secret-dependent branch. However, such techniques are only meaningful if they persist across compilation. In this paper, we investigate how optimizations used by modern compilers break the protections introduced by defensive programming techniques. Specifically, how compilers break high-level constant-time implementations used to mitigate timing side-channel attacks. We run a large-scale experiment to see if such compiler-induced issues manifest in state-of-the-art cryptographic libraries. We develop a tool that can profile virtually any architecture, and we use it to run trace-based dynamic analysis on 44,604 different targets. Particularly, we focus on the most widely deployed cryptographic libraries, which aim to provide side-channel resistance. We are able to evaluate whether their claims hold across various CPU architectures, including x86-64, x86-i386, armv7, aarch64, RISC-V, and MIPS-32. Our large-scale study reveals that several compiler-induced secret-dependent operations occur within some of the most highly regarded hardened cryptographic libraries. To the best of our knowledge, such findings represent the first time these issues have been observed in the wild. One of the key takeaways of this paper is that the state-of-the-art defensive programming techniques employed for side-channel resistance are still inadequate, incomplete, and bound to fail when paired with the optimizations that compilers continuously introduce.

cs.CR↗

arXiv2025

A Method for Rapid Area Prioritisation in Flood Disaster Response

In flood disasters, decision-makers have to rapidly prioritise the areas that need assistance based on a high volume of information. While approaches that combine GIS with Bayesian networks are generally effective in integrating multiple spatial variables and can thus reduce cognitive load, existing models in the literature are not equipped to address the time pressure and information-scape that is typical in a flood. To address the lack of a model for area prioritisation in flood disaster response, we present a novel decision support system that adheres to the time and information characteristics of an ongoing flood to infer the areas with the highest risk. This decision support system is based on a novel GIS-informed Bayesian network model that reflects the challenges of decision-making for area prioritisation. By developing the model during the preparedness phase, some of the most time-consuming aspects of the decision-making process are removed from the time-critical response phase. In this way, the proposed method aims to providing rapid and transparent area prioritisation recommendations for disaster response. To illustrate our method, we present a case study of an extreme flood scenario in Cologne, Germany.

stat.AP↗

arXiv2025

A Survey on LLM-Assisted Clinical Trial Recruitment

Recent advances in LLMs have greatly improved general-domain NLP tasks. Yet, their adoption in critical domains, such as clinical trial recruitment, remains limited. As trials are designed in natural language and patient data is represented as both structured and unstructured text, the task of matching trials and patients benefits from knowledge aggregation and reasoning abilities of LLMs. Classical approaches are trial-specific and LLMs with their ability to consolidate distributed knowledge hold the potential to build a more general solution. Yet recent applications of LLM-assisted methods rely on proprietary models and weak evaluation benchmarks. In this survey, we are the first to analyze the task of trial-patient matching and contextualize emerging LLM-based approaches in clinical trial recruitment. We critically examine existing benchmarks, approaches and evaluation frameworks, the challenges to adopting LLM technologies in clinical research and exciting future directions.

cs.CL↗

arXiv2025

Emergency Response Inference Mapping (ERIMap): A Bayesian network-based method for dynamic observation processing

In emergencies, high stake decisions often have to be made under time pressure and strain. In order to support such decisions, information from various sources needs to be collected and processed rapidly. The information available tends to be temporally and spatially variable, uncertain, and sometimes conflicting, leading to potential biases in decisions. Currently, there is a lack of systematic approaches for information processing and situation assessment which meet the particular demands of emergency situations. To address this gap, we present a Bayesian network-based method called ERIMap that is tailored to the complex information-scape during emergencies. The method enables the systematic and rapid processing of heterogeneous and potentially uncertain observations and draws inferences about key variables of an emergency. It thereby reduces complexity and cognitive load for decision makers. The output of the ERIMap method is a dynamically evolving and spatially resolved map of beliefs about key variables of an emergency that is updated each time a new observation becomes available. The method is illustrated in a case study in which an emergency response is triggered by an accident causing a gas leakage on a chemical plant site.

cs.IR↗

arXiv2025

Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory

Neural architectures such as Recurrent Neural Networks (RNNs), Transformers, and State-Space Models have shown great success in handling sequential data by learning temporal dependencies. Decision Trees (DTs), on the other hand, remain a widely used class of models for structured tabular data but are typically not designed to capture sequential patterns directly. Instead, DT-based approaches for time-series data often rely on feature engineering, such as manually incorporating lag features, which can be suboptimal for capturing complex temporal dependencies. To address this limitation, we introduce ReMeDe Trees, a novel recurrent DT architecture that integrates an internal memory mechanism, similar to RNNs, to learn long-term dependencies in sequential data. Our model learns hard, axis-aligned decision rules for both output generation and state updates, optimizing them efficiently via gradient descent. We provide a proof-of-concept study on synthetic benchmarks to demonstrate the effectiveness of our approach.

cs.LG↗

arXiv2025

The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning

Visual Reinforcement Learning (RL) methods often require extensive amounts of data. As opposed to model-free RL, model-based RL (MBRL) offers a potential solution with efficient data utilization through planning. Additionally, RL lacks generalization capabilities for real-world tasks. Prior work has shown that incorporating pre-trained visual representations (PVRs) enhances sample efficiency and generalization. While PVRs have been extensively studied in the context of model-free RL, their potential in MBRL remains largely unexplored. In this paper, we benchmark a set of PVRs on challenging control tasks in a model-based RL setting. We investigate the data efficiency, generalization capabilities, and the impact of different properties of PVRs on the performance of model-based agents. Our results, perhaps surprisingly, reveal that for MBRL current PVRs are not more sample efficient than learning representations from scratch, and that they do not generalize better to out-of-distribution (OOD) settings. To explain this, we analyze the quality of the trained dynamics model. Furthermore, we show that data diversity and network architecture are the most important contributors to OOD generalization performance.

cs.LG↗

arXiv2024

Dependent Infrastructure Service Disruption Mapping (DISruptionMap): A Method to Assess Cascading Service Disruptions in Disaster Scenarios

Critical infrastructures provide essential services for our modern society. Large-scale natural hazards, such as floods or storms, can disrupt multiple critical infrastructures at once. In addition, a localized failure of one service can trigger a cascade of failures of other dependent services. This makes it challenging to anticipate and prepare adequately for direct and indirect consequences of such events. Existing methods that are spatially explicit and consider service dependencies currently lack practicality, as they require large amounts of data. To address this gap, we propose a novel method called DISruptionMap which analyzes complex disruptions to critical infrastructure services. The proposed method combines i) spatial service models to assess direct service disruptions with ii) a service dependency model to assess indirect (cascading) service disruptions. A fault tree-based approach is implemented, resulting in a significant decrease in the information required to set up the service dependency model. We demonstrate the effectiveness of our method in a case study examining the impact of an extreme flood on health, transport, and power services in Cologne, Germany.

physics.soc-ph↗

arXiv2024

Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy

Cancer remains a leading cause of death, highlighting the importance of effective radiotherapy (RT). Magnetic resonance-guided linear accelerators (MR-Linacs) enable imaging during RT, allowing for inter-fraction, and perhaps even intra-fraction, adjustments of treatment plans. However, achieving this requires fast and accurate dose calculations. While Monte Carlo simulations offer accuracy, they are computationally intensive. Deep learning frameworks show promise, yet lack uncertainty quantification crucial for high-risk applications like RT. Risk-controlling prediction sets (RCPS) offer model-agnostic uncertainty quantification with mathematical guarantees. However, we show that naive application of RCPS may lead to only certain subgroups such as the image background being risk-controlled. In this work, we extend RCPS to provide prediction intervals with coverage guarantees for multiple subgroups with unknown subgroup membership at test time. We evaluate our algorithm on real clinical planing volumes from five different anatomical regions and show that our novel subgroup RCPS (SG-RCPS) algorithm leads to prediction intervals that jointly control the risk for multiple subgroups. In particular, our method controls the risk of the crucial voxels along the radiation beam significantly better than conventional RCPS.

cs.LG↗

arXiv2024

Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit's Showerthoughts

Recent Large Language Models (LLMs) have shown the ability to generate content that is difficult or impossible to distinguish from human writing. We investigate the ability of differently-sized LLMs to replicate human writing style in short, creative texts in the domain of Showerthoughts, thoughts that may occur during mundane activities. We compare GPT-2 and GPT-Neo fine-tuned on Reddit data as well as GPT-3.5 invoked in a zero-shot manner, against human-authored texts. We measure human preference on the texts across the specific dimensions that account for the quality of creative, witty texts. Additionally, we compare the ability of humans versus fine-tuned RoBERTa classifiers to detect AI-generated texts. We conclude that human evaluators rate the generated texts slightly worse on average regarding their creative quality, but they are unable to reliably distinguish between human-written and AI-generated texts. We further provide a dataset for creative, witty text generation based on Reddit Showerthoughts posts.

cs.CL↗

arXiv2023

It's TEEtime: A New Architecture Bringing Sovereignty to Smartphones

Modern smartphones are complex systems in which control over phone resources is exercised by phone manufacturers, OS vendors, and users. These stakeholders have diverse and often competing interests. Barring some exceptions, users entrust their security and privacy to OS vendors (Android and iOS) and need to accept their constraints. Manufacturers protect their firmware and peripherals from the OS by executing in the highest privilege and leveraging dedicated CPUs and TEEs. OS vendors need to trust the highest privileged code deployed by manufacturers. This division of control over the phone is not ideal for OS vendors and is even more disadvantageous for the users. Users are generally limited in what applications they can install on their devices, in the privacy model and trust assumptions of the existing applications, and in the functionalities that applications can have. We propose TEEtime, a new smartphone architecture based on trusted execution allowing to balance the control different stakeholders exert over phones. More leveled control over the phone means that no stakeholder is more privileged than the others. In particular, TEEtime makes users sovereign over their phones: It enables them to install sensitive applications in isolated domains with protected access to selected peripherals alongside an OS. TEEtime achieves this while maintaining compatibility with the existing smartphone ecosystem and without relying on virtualization; it only assumes trust in a phone's firmware. TEEtime is the first TEE architecture that allows isolated execution domains to gain protected and direct access to peripherals. TEEtime is based on Armv8-A and achieves peripheral isolation using a novel mechanism based on memory and interrupt controller protection. We demonstrate the feasibility of our design by implementing a prototype of TEEtime, and by running exemplary sensitive applications.

cs.CR↗

arXiv2023

A resilience glossary shaped by context: Reviewing resilience-related terms for critical infrastructures

We present a comprehensive resilience glossary, comprising a set of 91 definitions of resilience-related terms used in the context of critical infrastructures. The definition and use of many of these terms, as well as the term resilience itself, shows an enormous variability in the literature. Therefore, we draw from the diverse pool of published definitions, integrate multiple contrasting views, compare the individual terms, and provide references to adjoining or contesting views, to create a clear resilience terminology. This terminology outlines a specific understanding of resilience which supports the effective assessment and management of the resilience of critical infrastructures. The two central elements of this understanding are that (1) resilience is the ability of a system to deal with the impacts of unspecific and possibly unforeseen disruptive events, and that (2) this ability comprises three pillar capacities whose quality can be extracted from performance curves.

physics.soc-ph↗

arXiv2022

On (the Lack of) Code Confidentiality in Trusted Execution Environments

Trusted Execution Environments (TEEs) have been proposed as a solution to protect code confidentiality in scenarios where computation is outsourced to an untrusted operator. We study the resilience of such solutions to side-channel attacks in two commonly deployed scenarios: when a confidential code is a native binary that is shipped and executed within a TEE and when the confidential code is an intermediate representation (IR) executed on top of a runtime within a TEE. We show that executing IR code such as WASM bytecode on a runtime executing in a TEE leaks most IR instructions with high accuracy and therefore reveals the confidential code. Contrary to IR execution, native execution is much less susceptible to leakage and largely resists even the most powerful side-channel attacks. We evaluate native execution leakage in Intel SGX and AMD SEV and experimentally demonstrate end-to-end instruction extraction on Intel SGX, with WASM bytecode as IR executed within WAMR, a hybrid between a JIT compiler and interpreter developed by Intel. Our experiments show that IR code leakage from such systems is practical and therefore question the security claims of several commercial solutions which rely on TEEs+WASM for code confidentiality.

cs.CR↗

arXiv2022

SoK: Hardware-supported Trusted Execution Environments

The growing complexity of modern computing platforms and the need for strong isolation protections among their software components has led to the increased adoption of Trusted Execution Environments (TEEs). While several commercial and academic TEE architectures have emerged in recent times, they remain hard to compare and contrast. More generally, existing TEEs have not been subject to a holistic systematization to understand the available design alternatives for various aspects of TEE design and their corresponding pros-and-cons. Therefore, in this work, we analyze the design of existing TEEs and systematize the mechanisms that TEEs implement to achieve their security goals, namely, verifiable launch, run-time isolation, trusted IO, and secure storage. More specifically, we analyze the typical architectural building blocks underlying TEE solutions, design alternatives for each of these components and the trade-offs that they entail. We focus on hardware-assisted TEEs and cover a wide range of TEE proposals from academia and the industry. Our analysis shows that although TEEs are diverse in terms of their goals, usage models, and instruction set architectures, they all share many common building blocks in terms of their design.

cs.CR↗

arXiv2021

Composite Enclaves: Towards Disaggregated Trusted Execution

The ever-rising computation demand is forcing the move from the CPU to heterogeneous specialized hardware, which is readily available across modern datacenters through disaggregated infrastructure. On the other hand, trusted execution environments (TEEs), one of the most promising recent developments in hardware security, can only protect code confined in the CPU, limiting TEEs' potential and applicability to a handful of applications. We observe that the TEEs' hardware trusted computing base (TCB) is fixed at design time, which in practice leads to using untrusted software to employ peripherals in TEEs. Based on this observation, we propose \emph{composite enclaves} with a configurable hardware and software TCB, allowing enclaves access to multiple computing and IO resources. Finally, we present two case studies of composite enclaves: i) an FPGA platform based on RISC-V Keystone connected to emulated peripherals and sensors, and ii) a large-scale accelerator. These case studies showcase a flexible but small TCB (2.5 KLoC for IO peripherals and drivers), with a low-performance overhead (only around 220 additional cycles for a context switch), thus demonstrating the feasibility of our approach and showing that it can work with a wide range of specialized hardware.

cs.CR↗

arXiv2021

Frontal Attack: Leaking Control-Flow in SGX via the CPU Frontend

We introduce a new timing side-channel attack on Intel CPU processors. Our Frontal attack exploits timing differences that arise from how the CPU frontend fetches and processes instructions while being interrupted. In particular, we observe that in modern Intel CPUs, some instructions' execution times will depend on which operations precede and succeed them, and on their virtual addresses. Unlike previous attacks that could only profile branches if they contained different code or had known branch targets, the Frontal attack allows the adversary to distinguish between instruction-wise identical branches. As the attack requires OS capabilities to set the interrupts, we use it to exploit SGX enclaves. Our attack further demonstrates that secret-dependent branches should not be used even alongside defenses to current controlled-channel attacks. We show that the adversary can use the Frontal attack to extract a secret from an SGX enclave if that secret was used as a branching condition for two instruction-wise identical branches. We successfully tested the attack on all the available Intel CPUs with SGX (until 10th gen) and used it to leak information from two commonly used cryptographic libraries.

cs.CR↗

arXiv2021

Sovereign Smartphone: To Enjoy Freedom We Have to Control Our Phones

The majority of smartphones either run iOS or Android operating systems. This has created two distinct ecosystems largely controlled by Apple and Google - they dictate which applications can run, how they run, and what kind of phone resources they can access. Barring some exceptions in Android where different phone manufacturers may have influence, users, developers, and governments are left with little to no choice. Specifically, users need to entrust their security and privacy to OS vendors and accept the functionality constraints they impose. Given the wide use of Android and iOS, immediately leaving these ecosystems is not practical, except in niche application areas. In this work, we draw attention to the magnitude of this problem and why it is an undesirable situation. As an alternative, we advocate the development of a new smartphone architecture that securely transfers the control back to the users while maintaining compatibility with the rich existing smartphone ecosystems. We propose and analyze one such design based on advances in trusted execution environments for ARM and RISC-V.

cs.CR↗