DEEPTYPE: Refining Indirect Call Targets with Strong Multi-layer Type Analysis

Indirect calls, while facilitating dynamic execution characteristics in C and C++ programs, impose challenges on precise construction of the control-flow graphs (CFG). This hinders effective program analyses for bug detection (e.g., fuzzing) and program protection (e.g., control-flow integrity). Solutions using data-tracking and type-based analysis are proposed for identifying indirect call targets, but are either time-consuming or imprecise for obtaining the analysis results. Multi-layer type analysis (MLTA), as the state-of-the-art approach, upgrades type-based analysis by leveraging multi-layer type hierarchy, but their solution to dealing with the information flow between multi-layer types introduces false positives. In this paper, we propose strong multi-layer type analysis (SMLTA) and implement the prototype, DEEPTYPE, to further refine indirect call targets. It adopts a robust solution to record and retrieve type information, avoiding information loss and enhancing accuracy. We evaluate DEEPTYPE on Linux kernel, 5 web servers, and 14 user applications. Compared to TypeDive, the prototype of MLTA, DEEPTYPE is able to narrow down the scope of indirect call targets by 43.11% on average across most benchmarks and reduce runtime overhead by 5.45% to 72.95%, which demonstrates the effectiveness, efficiency and applicability of SMLTA.