Jérémie Decouchant (Delft University of Technology), David Kozhaya (ABB Corporate Research), Vincent Rahli (University of Birmingham), Jiangshan Yu (The University of Sydney)

Several Byzantine Fault-Tolerant (BFT) consensus algorithms leverage trusted components to boost resilience and reduce communication overhead. However, recent findings expose a critical vulnerability to rollback attacks when trusted components crash, lose state, or be cloned. Existing defenses either treat crashed replicas as Byzantine, increasing replica count, or duplicate trusted state across components, incurring substantial performance costs and offering limited crash tolerance.
We propose a robust alternative: a secure state-preservation mechanism for trusted components that eliminates costly duplication of trusted states across replicas. At its core is Aegis, the first efficient view synchronizer specifically designed for BFT protocols that utilize trusted components. Aegis enforces that only one trusted component instance per replica may vote in any view, even when trusted components restart following a crash or are cloned by an adversary. On top of Aegis, we introduce Pallas, the first BFT consensus protocol that preserves safety against a strong adversary that controls a fixed set of Byzantine replicas and can cause a potentially unbounded and varying number of trusted components to crash. We determine the adversarial conditions under which Pallas ensure liveness under partial synchrony.
Extensive geo-distributed evaluations on Amazon AWS show that Pallas delivers high performance with negligible overhead in stable conditions, outperforming existing protocols by up to 41% in throughput and 29% in latency. More importantly, it sustains liveness and graceful degradation under adversarial conditions where other protocols fail.

View More Papers

UsersFirst in Practice: Evaluating a User-Centric Threat Modeling Taxonomy...

Alexandra Xinran Li (Carnegie Mellon University), Tian Wang (University of Illinois Urbana-Champaign), Yu-Ju Yang (University of Illinois Urbana-Champaign), Miguel Rivera-Lanas (Carnegie Mellon University), Debeshi Ghosh (Carnegie Mellon University), Hana Habib (Carnegie Mellon University), Lorrie Cranor (Carnegie Mellon University), Norman Sadeh (Carnegie Mellon University)

Read More

Dataset Reduction and Watermark Removal via Self-supervised Learning for...

Hao Luan (Institute of Big Data, Fudan University, Shanghai, China and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China), Xue Tan (Institute of Big Data, Fudan University, Shanghai, China and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China), Zhiheng Li (School of Control Science and Engineering, Shandong University, Jinan,…

Read More

PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using...

Sayak Saha Roy (The University of Texas at Arlington), Shirin Nilizadeh (The University of Texas at Arlington)

Read More