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This paper presents TetraBFT, a novel unauthenticated Byzantine fault tolerant protocol for solving consensus in partial synchrony, eliminating the need for public key cryptography and ensuring resilience against computationally unbounded adversaries. TetraBFT has several compelling features: it necessitates only constant local storage, has optimal communication complexity, satisfies optimistic responsiveness -- allowing the protocol to operate at actual network speeds under ideal conditions -- and can achieve consensus in just 5 message delays, which outperforms all known unauthenticated protocols achieving the other properties listed. We validate the correctness of TetraBFT through rigorous security analysis and formal verification. Furthermore, we extend TetraBFT into a multi-shot, chained consensus protocol, making a pioneering effort in applying pipelining techniques to unauthenticated protocols. This positions TetraBFT as a practical and deployable solution for blockchain systems aiming for high efficiency.
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Unauthenticated Byzantine consensus protocols achieve optimal failure resilience while relying only on authenticated point-to-point channels, not authenticated messages. They are an attractive building block for blockchains that do not mandate symmetric trust assumptions as well as for future post-quantum settings. We consider unauthenticated Byzantine consensus in partially synchronous networks and focus on optimizing its good-case latency - the worst-case time for correct processes to reach a decision under favorable conditions. A recently proposed ForgetIT protocol achieves an optimal good-case latency of 3 message delays but employs a highly complex design. We show that this complexity is unnecessary. To this end, we present Fast TetraBFT - an unauthenticated Byzantine consensus protocol that achieves optimal good-case latency by augmenting an existing TetraBFT protocol with a simple fast-path wrapper. Our solution lowers the good-case latency of TetraBFT from 5 to 3 message delays while preserving its bounded space requirements and low communication complexity.
5G base stations broadcast unauthenticated system information (SI) that every user equipment (UE) reads during cell selection. This enables attackers to broadcast forged SI from a fake base station (FBS), deceiving UEs into camping on it. Prior approaches require UEs to authenticate System Information Block 1 (SIB1) using digital signatures. This necessitates computation-heavy verification for every SIB1 reception, imposing a significant burden on resource-constrained UEs. We propose TESLA-for-5G (TF5), a broadcast authentication protocol for 5G SIB1 that combines TESLA with GG09 Schnorr-like identity-based signatures (IBS). In the steady state, TF5 enables UEs to authenticate each SIB1 message using a symmetric MAC and delayed key disclosure, eliminating the need for per-message digital signatures. Initial trust is bootstrapped during cell entry using a lightweight GG09 IBS over the TESLA parameters, avoiding certificate distribution overhead. We formally verify TF5 in Tamarin under a Dolev-Yao adversary and demonstrate its favorable computation, communication, and storage costs through both an implementation on the OpenAirInterface 5G stack and trace-driven analysis.
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,
Is robot cybersecurity broken by AI? Consumer robots -- from autonomous lawnmowers to powered exoskeletons and window cleaners -- are rapidly entering homes and workplaces, yet their security remains rooted in assumptions of specialized attacker expertise. This paper presents evidence that Generative AI has fundamentally disrupted robot cybersecurity: what historically required deep knowledge of ROS, ROS 2, and robotic system internals can now be automated by anyone with access to state-of-the-art GenAI tools spearheaded by the open source CAI (Cybersecurity AI). We provide empirical evidence through three case studies: (1) compromising a Hookii autonomous lawnmower robot, uncovering fleet-wide vulnerabilities and data protection violations affecting 267+ connected devices, (2) exploiting a Hypershell powered exoskeleton, demonstrating safety-critical motor control weaknesses and credential exposure including access to over 3,300 internal support emails, and (3) breaching a HOBOT S7 Pro window cleaning robot, achieving unauthenticated BLE command injection and OTA firmware exploitation. Across these platforms, CAI discovered in an automated manner 38 vulnerabilities that would have