A Cross-Layer Fault Injection Framework for Cascading Failure Analysis in Blockchain Systems

Blockchain systems underpin a growing class of decentralized applications where data integrity, fault tolerance, and consensus reliability are critical operational requirements. Despite their architectural strengths, real-world blockchain deployments remain susceptible to complex failure modes that span multiple system layers simultaneously. Existing reliability studies predominantly examine faults in isolation — targeting either smart contract logic, consensus protocol behaviour, or network communication — without accounting for the interdependencies that exist across these layers [14]. This narrow scope leaves a critical gap: cascading failures that originate in one layer and propagate through others remain poorly understood and systematically underexplored. This work addresses that gap by proposing a cross-layer fault injection framework that introduces controlled, reproducible faults across the application, consensus, and network layers within a unified simulation environment. The framework incorporates a fault propagation methodology that traces how localised faults amplify into system-wide degradation across layer boundaries. Evaluation across eight fault scenarios — ranging from isolated single-layer faults to simultaneous three-layer injection — produced measurable and consistent results. Cross-layer fault conditions increased transaction confirmation latency by 490% over baseline, reduced throughput by 80.3%, raised the fork rate to 12.7 per 100 blocks, and produced an error rate of 14.3%. These results demonstrate that single-layer fault analysis underestimates true system vulnerability by margins exceeding 70% across key performance metrics. The findings establish that holistic multi-layer fault analysis reveals vulnerability classes that single-layer methods cannot detect, and that the proposed framework provides a rigorous and reproducible foundation for evaluating end-to-end blockchain resilience.