Sun Hyoung Kim (Penn State), Cong Sun (Xidian University), Dongrui Zeng (Penn State), Gang Tan (Penn State)

Enforcing fine-grained Control-Flow Integrity (CFI) is critical for increasing software security. However, for commercial off-the-shelf (COTS) binaries, constructing high-precision Control-Flow Graphs (CFGs) is challenging, because there is no source-level information, such as symbols and types, to assist in indirect-branch target inference. The lack of source-level information brings extra challenges to inferring targets for indirect calls compared to other kinds of indirect branches. Points-to analysis could be a promising solution for this problem, but there is no practical points-to analysis framework for inferring indirect call targets at the binary level. Value set analysis (VSA) is the state-of-the-art binary-level points-to analysis but does not scale to large programs. It is also highly conservative by design and thus leads to low-precision CFG construction. In this paper, we present a binary-level points-to analysis framework called BPA to construct sound and high-precision CFGs. It is a new way of performing points-to analysis at the binary level with the focus on resolving indirect call targets. BPA employs several major techniques, including assuming a block memory model and a memory access analysis for partitioning memory into blocks, to achieve a better balance between scalability and precision. In evaluation, we demonstrate that BPA achieves a 34.5% precision improvement rate over the current state-of-the-art technique without introducing false negatives.

View More Papers

Detecting Kernel Memory Leaks in Specialized Modules with Ownership...

Navid Emamdoost (University of Minnesota), Qiushi Wu (University of Minnesota), Kangjie Lu (University of Minnesota), Stephen McCamant (University of Minnesota)

Read More

Favocado: Fuzzing the Binding Code of JavaScript Engines Using...

Sung Ta Dinh (Arizona State University), Haehyun Cho (Arizona State University), Kyle Martin (North Carolina State University), Adam Oest (PayPal, Inc.), Kyle Zeng (Arizona State University), Alexandros Kapravelos (North Carolina State University), Gail-Joon Ahn (Arizona State University and Samsung Research), Tiffany Bao (Arizona State University), Ruoyu Wang (Arizona State University), Adam Doupe (Arizona State University),…

Read More

As Strong As Its Weakest Link: How to Break...

Kai Li (Syracuse University), Jiaqi Chen (Syracuse University), Xianghong Liu (Syracuse University), Yuzhe Tang (Syracuse University), XiaoFeng Wang (Indiana University Bloomington), Xiapu Luo (Hong Kong Polytechnic University)

Read More