Srdjan Čapkun

Sheila Zingg, Daniele Lain, Yoshimichi Nakatsuka et al.

The European Union will introduce the EUDI Wallet by late 2026, which allows users to hold digital credentials (i.e., representations of physical official identity documents) on their devices. This will allow users to securely and privately disclose identity attributes to websites. Although such a system has many benefits, it also introduces risks caused by poor credential disclosure decisions. In this paper, we (i) conduct a large-scale survey on credential disclosure with users and experts and (ii) evaluate the effectiveness and feasibility of our Credential Assistant that displays expert recommendations and user opinions. Our results show that users are likely to overshare (e.g., ~20% of users disclosed their official ID to news websites). This indicates that users struggle to protect their privacy, which will impact the usability of the EUDI Wallet and lead to privacy violations, identity theft, and other abuses of leaked credentials. Finally, we show that our Credential Assistant significantly reduces users' credential disclosure mistakes from ~15% to ~7%. However, it does not fully eliminate poor credential disclosure decisions, indicating that stronger interventions may be necessary, especially for sensitive attributes.

Claudio Anliker, Daniele Coppola, Giovanni Camurati et al.

Time-of-arrival (ToA) estimation via cross-correlation is an essential building block of time-of-flight ranging. However, in narrowband systems, it is notoriously difficult to protect against distance-decreasing attacks such as Early-Detect/Late-Commit (ED/LC). We present and analyze two new attacks that reshape ranging signals to compromise correlation-based ToA estimation. The first attack multiplies the signal by a symbol-periodic waveform in the time domain, while the second passes it through a negative group delay (NGD) filter. In contrast to ED/LC, our attacks do not require real-time symbol detection or adaptive compensation; they are completely symbol-agnostic. We describe implementation strategies for both attacks and discuss NGD filtering in the context of Bluetooth Channel Sounding (CS), a recent narrowband ranging system. To this end, we simulate an NGD circuit in LTspice and a ToA estimator in MATLAB, demonstrating that the attack can result in distance reductions of up to 18 m against Bluetooth CS RTT ranging. Finally, we verify the feasibility of the NGD approach by building a prototype using commercial off-the-shelf components.

Friederike Groschupp, Daniele Lain, Aritra Dhar et al.

Precise access control decisions are crucial to the security of both traditional applications and emerging agent-based systems. Typically, these decisions are made by users during app installation or at runtime. Due to the increasing complexity and automation of systems, making these access control decisions can add a significant cognitive load on users, often overloading them and leading to suboptimal or even arbitrary access control decisions. To address this problem, we propose to leverage the processing and reasoning capabilities of large language models (LLMs) to make dynamic, context-aware decisions aligned with the user's security preferences. For this purpose, we conducted a user study, which resulted in a dataset of 307 natural-language privacy statements and 14,682 access control decisions made by users. We then compare these decisions against those made by two versions of LLMs: a general and a personalized one, for which we also gathered user feedback on 1,446 of its decisions. Our results show that in general, LLMs can reflect users' preferences well, achieving up to 86\% accuracy when compared to the decision made by the majority of users. Our study also reveals a crucial trade-off in personalizing such a system: while providing user-specific privacy preferences to the LLM generally improves agreement with individual user decisions, adhering to those preferences can also violate some security best practices. Based on our findings, we discuss design and risk considerations for implementing a practical natural-language-based access control system that balances personalization, security, and utility.

Carmela Troncoso, Mathias Payer, Jean-Pierre Hubaux et al.

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.

Ivan Puddu, Moritz Schneider, Miro Haller et al.

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.