arXiv subjects

Bart Preneel

Publications and source records attributed to Bart Preneel.

At least 19 recordsLinked to original sources

arXiv2026

Security evaluation of quantum distance-bounding protocols via semidefinite programming

Quantum distance-bounding (QDB) protocols let a verifier check that a prover is both genuine and physically nearby. During a timed fast phase of quantum communication, the verifier measures round-trip times to obtain an upper bound on the prover's distance. For a uniform comparison, we isolate the fast phase and study one-round distance-fraud (DF) and mafia-fraud (MF) games. For discrete-variable QDB, we show that these games reduce to convex optimization problems and can therefore be solved exactly with semidefinite programming; each MF value comes with an explicit attack achieving it and a matching certificate that no attack does better. This contrasts with quantum position verification, where an attack is split between two separated parties, so its optimization is nonconvex and analyses rely on relaxations. In our MF game, the cooperating pair collapses to a single sequential strategy, which keeps the game convex and its exact value computable. Across the discrete-variable protocols we examine, the best one-round DF attack succeeds with the same probability ($1/2$) for every protocol, whereas MF clearly separates the protocols. For continuous-variable QDB, we report estimated attack success probabilities from a calibrated Gaussian attack model. The benchmark covers protocols whose fast phase itself authenticates the prover; designs that follow Brands and Chaum and instead bind the fast phase with a final authenticated message, like the earliest QDB proposal, fall outside it and are treated separately. Of the four protocols studied, two had no previously known one-round attack values, and we report the first ones; for the other two, we find MF attacks with higher success probability than previously reported. Overall, one-round MF resistance depends on whether an attacker can use information revealed early by the prover to answer a fresh challenge from the verifier.

quant-ph↗

arXiv2026

Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization

Gradient-based adversarial attacks remain a dominant threat to deep neural networks (DNNs), as they exploit gradient information to efficiently optimize adversarial perturbations. To address this, we investigate whether reinforcement learning (RL) training can disrupt the gradient structure used by attackers by training image classifiers with policy-gradient objectives and epsilon-greedy exploration. Through systematic experiments across CIFAR-10, CIFAR-100, and ImageNet-100 with multiple architectures, we find that RL-trained classifiers significantly disrupt gradient-based adversarial optimization. To explain this, we conduct a comprehensive mechanism analysis using loss landscape visualization, static and dynamic gradient indicators, and predictive entropy. Our analysis reveals that RL acts as an implicit regularizer, producing models with highly unstable gradient directions and smaller gradient magnitudes. This combination makes each PGD step both unreliable in direction and limited in magnitude, causing gradient-based attacks to fail within practical iteration budgets. We further show that combining RL with adversarial training (RL-adv) provides a dual-layer defense operating at two complementary levels: RL degrades gradient information available to attackers (gradient-level defense), while adversarial training strengthens decision boundaries (boundary-level defense). RL-adv achieves the highest robustness across all major attack types evaluated, including gradient-based (PGD, AutoAttack), transfer-based, and query-based attacks, outperforming SL-adv by a significant margin. These findings identify RL-induced gradient disruption as a complementary robustness mechanism and motivate future research on hybrid SL-RL training schedules that combine SL's efficiency with RL's gradient-regularization properties.

cs.LG↗

arXiv2026

Post-Quantum Discovery as a Governance Capability: Evidence-Based Cryptographic Visibility and Exposure Prioritisation in a Critical Service Provider

Post Quantum Cryptography (PQC) readiness is increasingly constrained not by algorithm availability, but by cryptographic visibility, dependency complexity, and fragmented governance. This paper presents an anonymised case study of a large European critical service provider that initiated PQC readiness through a discovery first strategy, utilizing tool supported cryptographic inventorying to establish an evidence based baseline prior to migration planning. The discovery phase revealed systemic challenges, including distributed cryptographic ownership, uneven evidence quality across legacy and modern environments, and high dependency on third party cryptographic roadmaps. To operationalise these findings, the organisation introduced a structured exposure register that enabled prioritisation based on asset criticality, confidentiality longevity, and migration feasibility. We argue that PQC discovery should be understood as a governance capability that stabilises organisational knowledge and converts cryptographic uncertainty into measurable accountability, supporting risk based decision making and ecosystem coordination. The results contribute actionable lessons for institutions pursuing crypto-agility and resilience under post quantum harvest now, decrypt later threat models.

cs.CR↗

arXiv2026

Security Framework for Quantum Distance-Bounding

Distance-bounding (DB) protocols let a verifier upper-bound a prover's physical distance by timing rapid challenge-response exchanges. Quantum communication promises simpler DB protocols with stronger security guarantees, yet existing quantum distance-bounding (QDB) proposals are analysed in ad-hoc models and, to the best of our knowledge, lack a common game-based treatment of standard fraud attacks. We contribute (i) a reusable security framework for QDB that fixes system and timing assumptions, specifies a quantum-capable adversary model, formalises distance-, mafia-, and terrorist-fraud experiments, and includes a simple i.i.d. depolarizing noise model; and (ii) an application of this framework to a published QDB protocol. For this protocol we characterise the honest per-round acceptance probability under noise and lift it to the multi-round setting, yielding explicit completeness guarantees as a function of the number of fast rounds, the acceptance threshold, and the noise parameter. For active adversaries we bound the per-round success probability of distance-fraud attacks and analyse the best known mafia-fraud strategy, deriving corresponding multi-round soundness bounds. We also show that the protocol is inherently insecure against terrorist-fraud in our model. The framework cleanly separates protocol-independent definitions from protocol-specific analysis and can be used to evaluate existing and future QDB protocols on a common basis.

quant-ph↗

arXiv2026

Secure Wi-Fi Ranging Today: Security and Adoption of IEEE 802.11az/bk

Ranging and localisation have become critical for many applications and services. The Wi-Fi (IEEE 802.11) standard is a natural candidate for providing these functions across diverse environments, given its widespread deployment. The IEEE 802.11az amendment, finalised in 2023, introduces "Next Generation Positioning" mechanisms to secure and harden the existing insecure Wi-Fi Fine Timing Measurement (FTM) ranging solution. Moreover, the recent IEEE 802.11bk amendment increases the available bandwidth with the goal of approaching the centimetre-level ranging accuracy of ultra-wideband (UWB) systems. This paper examines to what extent these promises hold from a security and deployability perspective. We analyse the core mechanisms of secure Wi-Fi ranging as defined in IEEE 802.11az and IEEE 802.11bk at both the logical and physical layers, combining standards analysis with simulations and measurements on commercial and development hardware. At the logical layer, we show how common deployment choices can result in unauthenticated ranging, downgrade attacks, and simple denial-of-service attacks, making it difficult to securely realise many high-stakes use cases. At the physical layer, we study the predictability of secure ranging waveforms, the security impact of symbol repetition, and how waveform design choices affect compliance with spectral masks under realistic RF behaviour. Our results show that secure Wi-Fi ranging is highly sensitive to configuration choices and is non-trivial to implement on existing hardware. This is also evidenced by the currently limited support for secure Wi-Fi ranging in commodity devices. This paper provides practical guidelines for using secure FTM safely and recommendations to vendors and standardisation bodies to improve its robustness and deployability.

cs.CR↗

arXiv2025

ThreadFuzzer: Fuzzing Framework for Thread Protocol

With the rapid growth of IoT, secure and efficient mesh networking has become essential. Thread has emerged as a key protocol, widely used in smart-home and commercial systems, and serving as a core transport layer in the Matter standard. This paper presents ThreadFuzzer, the first dedicated fuzzing framework for systematically testing Thread protocol implementations. By manipulating packets at the MLE layer, ThreadFuzzer enables fuzzing of both virtual OpenThread nodes and physical Thread devices. The framework incorporates multiple fuzzing strategies, including Random and Coverage-based fuzzers from CovFuzz, as well as a newly introduced TLV Inserter, designed specifically for TLV-structured MLE messages. These strategies are evaluated on the OpenThread stack using code-coverage and vulnerability-discovery metrics. The evaluation uncovered five previously unknown vulnerabilities in the OpenThread stack, several of which were successfully reproduced on commercial devices that rely on OpenThread. Moreover, ThreadFuzzer was benchmarked against an oracle AFL++ setup using the manually extended OSS-Fuzz harness from OpenThread, demonstrating strong effectiveness. These results demonstrate the practical utility of ThreadFuzzer while highlighting challenges and future directions in the wireless protocol fuzzing research space.

cs.CR↗

arXiv2025

Impact of Data Duplication on Deep Neural Network-Based Image Classifiers: Robust vs. Standard Models

The accuracy and robustness of machine learning models against adversarial attacks are significantly influenced by factors such as training data quality, model architecture, the training process, and the deployment environment. In recent years, duplicated data in training sets, especially in language models, has attracted considerable attention. It has been shown that deduplication enhances both training performance and model accuracy in language models. While the importance of data quality in training image classifier Deep Neural Networks (DNNs) is widely recognized, the impact of duplicated images in the training set on model generalization and performance has received little attention. In this paper, we address this gap and provide a comprehensive study on the effect of duplicates in image classification. Our analysis indicates that the presence of duplicated images in the training set not only negatively affects the efficiency of model training but also may result in lower accuracy of the image classifier. This negative impact of duplication on accuracy is particularly evident when duplicated data is non-uniform across classes or when duplication, whether uniform or non-uniform, occurs in the training set of an adversarially trained model. Even when duplicated samples are selected in a uniform way, increasing the amount of duplication does not lead to a significant improvement in accuracy.

cs.LG↗

arXiv2025

Mining Power Destruction Attacks in the Presence of Petty-Compliant Mining Pools

Bitcoin's security relies on its Proof-of-Work consensus, where miners solve puzzles to propose blocks. The puzzle's difficulty is set by the difficulty adjustment mechanism (DAM), based on the network's available mining power. Attacks that destroy some portion of mining power can exploit the DAM to lower difficulty, making such attacks profitable. In this paper, we analyze three types of mining power destruction attacks in the presence of petty-compliant mining pools: selfish mining, bribery, and mining power distraction attacks. We analyze selfish mining while accounting for the distribution of mining power among pools, a factor often overlooked in the literature. Our findings indicate that selfish mining can be more destructive when the non-adversarial mining share is well distributed among pools. We also introduce a novel bribery attack, where the adversarial pool bribes petty-compliant pools to orphan others' blocks. For small pools, we demonstrate that the bribery attack can dominate strategies like selfish mining or undercutting. Lastly, we present the mining distraction attack, where the adversarial pool incentivizes petty-compliant pools to abandon Bitcoin's puzzle and mine for a simpler puzzle, thus wasting some part of their mining power. Similar to the previous attacks, this attack can lower the mining difficulty, but with the difference that it does not generate any evidence of mining power destruction, such as orphan blocks.

cs.CR↗

arXiv2024

Bitcoin under Volatile Block Rewards: How Mempool Statistics Can Influence Bitcoin Mining

The security of Bitcoin protocols is deeply dependent on the incentives provided to miners, which come from a combination of block rewards and transaction fees. As Bitcoin experiences more halving events, the protocol reward converges to zero, making transaction fees the primary source of miner rewards. This shift in Bitcoin's incentivization mechanism, which introduces volatility into block rewards, leads to the emergence of new security threats or intensifies existing ones. Previous security analyses of Bitcoin have either considered a fixed block reward model or a highly simplified volatile model, overlooking the complexities of Bitcoin's mempool behavior. This paper presents a reinforcement learning-based tool to develop mining strategies under a more realistic volatile model. We employ the Asynchronous Advantage Actor-Critic (A3C) algorithm, which efficiently handles dynamic environments, such as the Bitcoin mempool, to derive near-optimal mining strategies when interacting with an environment that models the complexity of the Bitcoin mempool. This tool enables the analysis of adversarial mining strategies, such as selfish mining and undercutting, both before and after difficulty adjustments, providing insights into the effects of mining attacks in both the short and long term. We revisit the Bitcoin security threshold presented in the WeRLman paper and demonstrate that the implicit predictability of valuable transaction arrivals in this model leads to an underestimation of the reported threshold. Additionally, we show that, while adversarial strategies like selfish mining under the fixed reward model incur an initial loss period of at least two weeks, the transition toward a transaction-fee era incentivizes mining pools to abandon honest mining for immediate profits. This incentive is expected to become more significant as the protocol reward approaches zero in the future.

cs.CR↗

arXiv2024

CovFUZZ: Coverage-based fuzzer for 4G&5G protocols

4G and 5G represent the current cellular communication standards utilized daily by billions of users for various applications. Consequently, ensuring the security of 4G and 5G network implementations is critically important. This paper introduces an automated fuzzing framework designed to test the security of 4G and 5G attach procedure implementations. Our framework provides a comprehensive solution for uplink and downlink fuzzing in 4G, as well as downlink fuzzing in 5G, while supporting fuzzing on all layers except the physical layer. To guide the fuzzing process, we introduce a novel algorithm that assigns probabilities to packet fields and adjusts these probabilities based on coverage information from the device-under-test (DUT). For cases where coverage information from the DUT is unavailable, we propose a novel methodology to estimate it. When evaluating our framework, we first run the random fuzzing experiments, where the mutation probabilities are fixed throughout the fuzzing, and give an insight into how those probabilities should be chosen to optimize the Random fuzzer to achieve the best coverage. Next, we evaluate the efficiency of the proposed coverage-based algorithms by fuzzing open-source 4G stack (srsRAN) instances and show that the fuzzer guided by our algorithm outperforms the optimized Random fuzzer in terms of DUT's code coverage. In addition, we run fuzzing tests on 12 commercial off-the-shelf (COTS) devices. In total, we discovered vulnerabilities in 10 COTS devices and all of the srsRAN 4G instances.

cs.CR↗

arXiv2024

Commitment Attacks on Ethereum's Reward Mechanism

Validators in permissionless, large-scale blockchains, such as Ethereum, are typically payoff-maximizing, rational actors. Ethereum relies on in-protocol incentives, like rewards for correct and timely votes, to induce honest behavior and secure the blockchain. However, external incentives, such as the block proposer's opportunity to capture maximal extractable value (MEV), may tempt validators to deviate from honest protocol participation. We show a series of commitment attacks on LMD GHOST, a core part of Ethereum's consensus mechanism. We demonstrate how a single adversarial block proposer can orchestrate long-range chain reorganizations by manipulating Ethereum's reward system for timely votes. These attacks disrupt the intended balance of power between proposers and voters: by leveraging credible threats, the adversarial proposer can coerce voters from previous slots into supporting blocks that conflict with the honest chain, enabling a chain reorganization. In response, we introduce a novel reward mechanism that restores the voters' role as a check against proposer power. Our proposed mitigation is fairer and more decentralized, not only in the context of these attacks, but also practical for implementation in Ethereum.

cs.CR↗

arXiv2024

GitHub Copilot: the perfect Code compLeeter?

This paper aims to evaluate GitHub Copilot's generated code quality based on the LeetCode problem set using a custom automated framework. We evaluate the results of Copilot for 4 programming languages: Java, C++, Python3 and Rust. We aim to evaluate Copilot's reliability in the code generation stage, the correctness of the generated code and its dependency on the programming language, problem's difficulty level and problem's topic. In addition to that, we evaluate code's time and memory efficiency and compare it to the average human results. In total, we generate solutions for 1760 problems for each programming language and evaluate all the Copilot's suggestions for each problem, resulting in over 50000 submissions to LeetCode spread over a 2-month period. We found that Copilot successfully solved most of the problems. However, Copilot was rather more successful in generating code in Java and C++ than in Python3 and Rust. Moreover, in case of Python3 Copilot proved to be rather unreliable in the code generation phase. We also discovered that Copilot's top-ranked suggestions are not always the best. In addition, we analysed how the topic of the problem impacts the correctness rate. Finally, based on statistics information from LeetCode, we can conclude that Copilot generates more efficient code than an average human.

cs.SE↗

arXiv2024

Fast Evaluation of S-boxes with Garbled Circuits

Garbling schemes are vital primitives for privacy-preserving protocols and secure two-party computation. This paper presents a projective garbling scheme that assigns $2^n$ values to wires in a circuit comprising XOR and unary projection gates. A generalization of FreeXOR allows the XOR of wires with $2^n$ values to be very efficient. We then analyze the performance of our scheme by evaluating substitution-permutation ciphers. Using our proposal, we measure high-speed evaluation of the ciphers with a moderately increased cost in garbling and bandwidth. Theoretical analysis suggests that for evaluating the nine examined ciphers, one can expect a 4- to 70-fold improvement in evaluation performance with, at most, a 4-fold increase in garbling cost and, at most, an 8-fold increase in communication cost compared to the Half-Gates (Zahur, Rosulek and Evans; Eurocrypt'15) and ThreeHalves (Rosulek and Roy; Crypto'21) garbling schemes. In an offline/online setting, such as secure function evaluation as a service, the circuit garbling and communication to the evaluator can proceed in the offline phase. Thus, our scheme offers a fast online phase. Furthermore, we present efficient Boolean circuits for the S-boxes of TWINE and Midori64 ciphers. To our knowledge, our formulas give the smallest number of AND gates for the S-boxes of these two ciphers.

cs.CR↗

arXiv2022

A Survey of Security and Privacy Issues in V2X Communication Systems

Vehicle-to-Everything (V2X) communication is receiving growing attention from industry and academia as multiple pilot projects explore its capabilities and feasibility. With about 50\% of global road vehicle exports coming from the European Union (EU), and within the context of EU legislation around security and data protection, V2X initiatives must consider security and privacy aspects across the system stack, in addition to road safety. Contrary to this principle, our survey of relevant standards, research outputs, and EU pilot projects indicates otherwise; we identify multiple security and privacy related shortcomings and inconsistencies across the standards. We conduct a root cause analysis of the reasons and difficulties associated with these gaps, and categorize the identified security and privacy issues relative to these root causes. As a result, our comprehensive analysis sheds lights on a number of areas that require improvements in the standards, which are not explicitly identified in related work. Our analysis fills gaps left by other related surveys, which are focused on specific technical areas but not necessarily point out underlying root issues in standard specifications. We bring forward recommendations to address these gaps for the overall improvement of security and safety in vehicular communication.

cs.CR↗

arXiv2021

HERMES: Scalable, Secure, and Privacy-Enhancing Vehicle Access System

We propose HERMES, a scalable, secure, and privacy-enhancing system for users to share and access vehicles. HERMES securely outsources operations of vehicle access token generation to a set of untrusted servers. It builds on an earlier proposal, namely SePCAR [1], and extends the system design for improved efficiency and scalability. To cater to system and user needs for secure and private computations, HERMES utilizes and combines several cryptographic primitives with secure multiparty computation efficiently. It conceals secret keys of vehicles and transaction details from the servers, including vehicle booking details, access token information, and user and vehicle identities. It also provides user accountability in case of disputes. Besides, we provide semantic security analysis and prove that HERMES meets its security and privacy requirements. Last but not least, we demonstrate that HERMES is efficient and, in contrast to SePCAR, scales to a large number of users and vehicles, making it practical for real-world deployments. We build our evaluations with two different multiparty computation protocols: HtMAC-MiMC and CBC-MAC-AES. Our results demonstrate that HERMES with HtMAC-MiMC requires only approx 1,83 ms for generating an access token for a single-vehicle owner and approx 11,9 ms for a large branch of rental companies with over a thousand vehicles. It handles 546 and 84 access token generations per second, respectively. This results in HERMES being 696 (with HtMAC-MiMC) and 42 (with CBC-MAC-AES) times faster compared to in SePCAR for a single-vehicle owner access token generation. Furthermore, we show that HERMES is practical on the vehicle side, too, as access token operations performed on a prototype vehicle on-board unit take only approx 62,087 ms.

cs.CR↗

arXiv2020

Towards a common performance and effectiveness terminology for digital proximity tracing applications

Digital proximity tracing (DPT) for Sars-CoV-2 pandemic mitigation is a complex intervention with the primary goal to notify app users about possible risk exposures to infected persons. Policymakers and DPT operators need to know whether their system works as expected in terms of speed or yield (performance) and whether DPT is making an effective contribution to pandemic mitigation (also in comparison to and beyond established mitigation measures, particularly manual contact tracing). Thereby, performance and effectiveness are not to be confused. Not only are there conceptual differences but also diverse data requirements. This article describes differences between performance and effectiveness measures and attempts to develop a terminology and classification system for DPT evaluation. We discuss key aspects for critical assessments of whether the integration of additional data measurements into DPT apps - beyond what is required to fulfill its primary notification role - may facilitate an understanding of performance and effectiveness of planned and deployed DPT apps. Therefore, the terminology and a classification matrix may offer some guidance to DPT system operators regarding which measurements to prioritize. DPT developers and operators may also make conscious decisions to integrate measures for epidemic monitoring but should be aware that this introduces a secondary purpose to DPT that is not part of the original DPT design. Ultimately, the integration of further information for epidemic monitoring into DPT involves a trade-off between data granularity and linkage on the one hand, and privacy on the other. Decision-makers should be aware of the trade-off and take it into account when planning and developing DPT notification and monitoring systems or intending to assess the added value of DPT relative to existing contact tracing systems.

cs.CY↗

arXiv2020

A Novel Demodulation Scheme for Secure and Reliable UWB Distance Bounding

Relay attacks pose an important threat in wireless ranging and authentication systems. Distance bounding protocols have been proposed as an effective countermeasure against these attacks and allow a verifier and a prover to establish an upper bound on the distance between them. However, secure distance bounding protocols are hard to realize in practice due to stringent implementation requirements. In this paper, we look into a yet unexplored research area and show how the security strength of Ultra Wide Band (UWB) distance bounding protocols can be significantly increased by imposing several additional security constraints during demodulation and decoding at the receiver. We demonstrate that for equal reliability metrics as in state-of-the-art UWB distance bounding protocols, our solution achieves a reduction of the success probability of a relay attack by a factor of 40. Moreover, we also argue that our security solution only needs to be combined with pulse masking and a distance commitment to achieve these security bounds and there is no need to have pulse reordering in our modulation.

cs.CR↗

arXiv2020

Decentralized Privacy-Preserving Proximity Tracing

This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale. This system, referred to as DP3T, provides a technological foundation to help slow the spread of SARS-CoV-2 by simplifying and accelerating the process of notifying people who might have been exposed to the virus so that they can take appropriate measures to break its transmission chain. The system aims to minimise privacy and security risks for individuals and communities and guarantee the highest level of data protection. The goal of our proximity tracing system is to determine who has been in close physical proximity to a COVID-19 positive person and thus exposed to the virus, without revealing the contact's identity or where the contact occurred. To achieve this goal, users run a smartphone app that continually broadcasts an ephemeral, pseudo-random ID representing the user's phone and also records the pseudo-random IDs observed from smartphones in close proximity. When a patient is diagnosed with COVID-19, she can upload pseudo-random IDs previously broadcast from her phone to a central server. Prior to the upload, all data remains exclusively on the user's phone. Other users' apps can use data from the server to locally estimate whether the device's owner was exposed to the virus through close-range physical proximity to a COVID-19 positive person who has uploaded their data. In case the app detects a high risk, it will inform the user.

cs.CR↗