CEDAR: Silent Control Flow Error Detection via Heterogeneous Relation Learning

Control flow errors (CFEs) are prevalent and de-structive runtime faults that compromise software reliability and security. Existing CFE detection approaches either perform coarse-grained modeling at the basic-block level, missing fine-grained implicit features, or rely on exhaustive fault injection for detailed error patterns. These limitations hinder efficient detection, especially for silent CFEs, where branches deviate from correct paths, yet still follow compiler-defined ones. To bridge this gap, we propose CEDAR, a novel CFE detection approach, which detects silent CFEs by localizing and hardening vulnerable instructions. Specifically, we first conduct multi-level control flow analysis with limited fault injection, enhanced by dynamic data propagation, to construct a dual-layer heterogeneous graph representation of the program. Subsequently, we employ Graph Neural Networks (GNNs) to learn silent CFE-relevant embeddings and develop a model to localize vulnerable instructions. Finally, targeted program hardening mechanisms are integrated to detect silent CFEs and improve overall CFE coverage. Experiments demonstrate that CEDAR achieves 92.28% coverage of silent CFEs and 96.47% on average for overall CFEs. While maintaining high coverage, it improves the Evaluation Factor (EF) by 21.97% over the state-of-the-art approach, achieving a favorable trade-off between effectiveness and overhead. More-over, it demonstrates robustness across diverse input scenarios and Instruction Set Architectures (ISAs).