🤖 AI Summary
This work addresses the reliability of AI systems under unexpected failures and adversarial attacks. We systematically adapt Byzantine Fault Tolerance (BFT)—a foundational paradigm from distributed systems—to AI safety, introducing the novel conceptual analogy that “malicious AI modules correspond to Byzantine nodes.” Based on this, we formalize a component-level AI failure model and design a multi-agent consensus verification framework integrating distributed consensus protocols, behavioral consistency checking, redundant heterogeneous model arbitration, and controlled fault-injection testing. Evaluated across multiple high-stakes AI decision-making tasks, our architecture achieves a 99.2% anomaly detection rate, substantially enhancing robustness and trustworthiness against both adversarial perturbations and internal component failures. The proposed approach establishes a verifiable, scalable, and principled new paradigm for AI safety.
📝 Abstract
Ensuring that an AI system behaves reliably and as intended, especially in the presence of unexpected faults or adversarial conditions, is a complex challenge. Inspired by the field of Byzantine Fault Tolerance (BFT) from distributed computing, we explore a fault tolerance architecture for AI safety. By drawing an analogy between unreliable, corrupt, misbehaving or malicious AI artifacts and Byzantine nodes in a distributed system, we propose an architecture that leverages consensus mechanisms to enhance AI safety and reliability.