Where Does It Go?: Refining Indirect-Call Targets with Multi-Layer Type Analysis

System software commonly uses indirect calls to realize dynamic program behaviors. However, indirect-calls also bring challenges to constructing a precise control-flow graph that is a standard prerequisite for many static program-analysis and system-hardening techniques. Unfortunately, identifying indirect-call targets is a hard problem. In particular, modern compilers do not recognize indirectcall targets by default. Existing approaches identify indirect-call targets based on type analysis that matches the types of function pointers and the ones of address-taken functions. Such approaches, however, suffer from a high false-positive rate as many irrelevant functions may share the same types. In this paper, we propose a new approach, namely Multi-Layer Type Analysis (MLTA), to effectively refine indirect-call targets for C/C++ programs. MLTA relies on an observation that function pointers are commonly stored into objects whose types have a multilayer type hierarchy; before indirect calls, function pointers will be loaded from objects with the same type hierarchy "layer by layer". By matching the multi-layer types of function pointers and functions, MLTA can dramatically refine indirect-call targets. MLTA is effective because multi-layer types are more restrictive than singlelayer types. It does not introduce false negatives by conservatively tracking targets propagation between multi-layer types, and the layered design allows MLTA to safely fall back whenever the analysis for a layer becomes infeasible. We have implemented MLTA in a system, namely TypeDive, based on LLVM and extensively evaluated it with the Linux kernel, the FreeBSD kernel, and the Firefox browser. Evaluation results show that TypeDive can eliminate 86% to 98% more indirect-call targets than existing approaches do, without introducing new false negatives. We also demonstrate that TypeDive not only improves the scalability of static analysis but also benefits semantic-bug detection. With TypeDive, we have found 35 new deep semantic bugs in the Linux kernel. CCS Concepts • Security and privacy → Systems security; Software and application security.