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

Forward and Backward Private Conjunctive Searchable Symmetric Encryption

Sikhar Patranabis (ETH Zurich), Debdeep Mukhopadhyay (IIT Kharagpur)

Read More

Denial-of-Service Attacks on C-V2X Networks

Natasa Trkulja, David Starobinski (Boston University), and Randall Berry (Northwestern University)

Read More

(Short) WIP: End-to-End Analysis of Adversarial Attacks to Automated...

Hengyi Liang, Ruochen Jiao (Northwestern University), Takami Sato, Junjie Shen, Qi Alfred Chen (UC Irvine), and Qi Zhu (Northwestern University) Best Short Paper Award Winner!

Read More