SAT-based techniques have become a powerful tool for automated differential and linear trail search in symmetric cryptanalysis. Their practical efficiency, however, depends strongly on both solver behavior and the way pruning information is encoded. In this work, we revisit SAT-based trail search from a solver-centric perspective, focusing on two aspects: lightweight portfolio-style parallelization and an end-toend evaluation of Matsui-based hybrid acceleration frameworks. First, using Kissat on PRESENT and RECTANGLE, we show that the --unsat preset usually gives the best overall performance in fullround bound computations, while the --sat preset does not consistently dominate even on satisfiable instances. Motivated by this, we propose a low-parallelism portfolio strategy that runs the --sat and --unsat presets in parallel and stops once either finishes. For PRESENT, RECTANGLE, and GIFT-64, this simple strategy typically reduces runtime to about 50%-70% of the --default baseline and can be competitive with, or even faster than, the portfolio solver Plingeling on longrunning instances. Second, we compare two Matsui-based acceleration frameworks by Sun et al. and Wang et al., explicitly including both SAT-solving time and the MILP overhead in Wang et al.'s two-stage method. Under matched resource constraints, the apparent advantage of the MILP-based approach is substantially reduced and can even disappear. Overall, our results underline the importance of holistic runtime evaluation for hybrid cryptanalytic frameworks and show that simple solver-level strategies can provide practical acceleration for SAT-based trail search.
The deployment of digital car keys on personal devices, such as smartphones, offers new convenience features and business models. On the downside, this new connectivity increases the cyberattack surface of connected vehicles and enables scalable cyberattacks leading to unauthorized access to the vehicle. In this work, we design an enhanced security concept for digital car keys, which maintains the security properties of existing digital car key ecosystems even under an extended cyberthreat model where an attacker may exploit a protocol weakness that we call the “Missing Link Vulnerability”. Our concept mitigates against this attack by cryptographically binding the device authentication and, optionally, sensor readings for distance bounding, to the vehicle access authorization step. All policy decisions are performed within the secure environment of a Trusted Platform Module (TPM) security chip to achieve additional protection against runtime and (hardware) side-channel attacks.
Large Language Models (LLMs) utilize the Model Context Protocol (MCP) to interact with external tools; however, the protocol’s design lacks inherent security mechanisms. This absence of security creates risks when MCP servers are deployed in untrusted environments, leaving them vulnerable to attacks that could violate authenticity, integrity, and confidentiality. To address these risks, we propose ShieldMCP, a framework to leverage hardware-based Trusted Execution Environments (TEEs) for securing MCP servers. Instead of encapsulating the entire server within a TEE, which would introduce performance overhead and an inflated trusted computing base, our approach applies fine-grained TEE protection at the tool function level, which an LLM can call to perform specific external actions such as querying a database, reading and writing files, or fetching web pages. ShieldMCP employs an automated pipeline: it first scans for tool functions in the MCP server and generates corresponding unit tests using an LLM agent. It then performs dynamic analysis to trim each function’s code. Finally, the resulting smaller artifact is packaged into a TEE and deployed with a secure, attested invocation channel. Our results show that the LLM agent achieved an average test coverage of 97
Young adults routinely encounter privacy notices and other legal documents on the Web, a challenge exacerbated by emerging frameworks such as the EU Data Act, which formalize complex data-sharing rights. Visual signals offer a promising means of revealing these properties, yet empirical evidence of how young users interpret them remains limited. We conducted an online study with 94 university students to examine the congruence between icons and labels, recall, and semantic interpretation of pictograms and color cues intended to convey privacy risks. Participants rated the representativeness of candidate symbols and evaluated color associations (e.g., danger, reassurance) before completing a label-free recall task. Results reveal a dichotomy: concrete pictograms (e.g., camera, location) showed high congruence and remembrance, whereas abstract “data collection” symbols performed markedly worse. Demographic effects were negligible, suggesting cross-group stability. We discuss design implications for youth-oriented interfaces, such as app stores and consent dialogs. We outline how these findings can inform the development of icon systems.
Mobile healthcare (mHealth) empowers individuals to generate and share their Personal Health Records (PHRs) to facilitate remote telemedicine services, leveraging advanced technologies such as the Internet of Things (IoT) and cloud computing. To ensure secure data sharing in public clouds, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been widely adopted for fine-grained access control over sensitive PHR data. However, malicious yet authorized users may still leak their decryption keys for illicit financial gain, underscoring the need for traceability and timely revocation of such misbehaving entities. Moreover, most existing CP-ABE schemes support only binary or multi-valued attributes, lacking the expressiveness of weighted attributes, which are crucial for fine-grained policy representation in healthcare scenarios. In this paper, we propose a Searchable Weighted Attribute-based Construction with Traceability (SWACT). In SWACT, each attribute is characterized by both its name and weight, enabling more flexible and expressive access policies while reducing policy complexity without expanding ciphertext size. The scheme incorporates a traceability mechanism to identify malicious users and employs a user binary tree structure to support efficient and scalable direct revocation. Additionally, SWACT enables authorized keyword search and outsourced decryption, significantly reducing computational and communication overhead on the user side. Extensive security analysis and performance evaluation demonstrate that SWACT achieves robust security, high efficiency, and enhanced functionality, making it well-suited for practical mHealth systems.
