Alternating Control Flow Graph Reconstruction by Combining Constant Propagation and Strided Intervals with Directed Symbolic Execution

Abstract: In this thesis we address the problem of control flow reconstruction in the presence of indirect jumps. We introduce an alternating approach which combines both overand under-approximation to increase the precision of the reconstructed control flow automaton compared to pure over-approximation. More specifically, the abstract interpretation based tool, Jakstab, from earlier work by Kinder, is used for the over-approximation. Furthermore, directed symbolic execution is applied to under-approximate successors of instructions when these can not be over-approximated precisely. The results of our experiments show that our approach can improve the precision of the reconstructed CFA compared to only using Jakstab. However, they reveal that our approach consumes a large amount of memory since it requires extraction and temporary storage of a large number of possible paths to the unresolved locations. As such, its usability is limited to control flow automatas of limited complexity. Further, the results show that strided interval analysis suffers in performance when encountering particularly challenging loops in the control flow automaton.