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

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 47 ) ) ) [post__not_in] => Array ( [0] => 6943 ) )

Screen Gleaning: A Screen Reading TEMPEST Attack on Mobile...

Zhuoran Liu (Radboud university), Niels Samwel (Radboud University), Léo Weissbart (Radboud University), Zhengyu Zhao (Radboud University), Dirk Lauret (Radboud University), Lejla Batina (Radboud University), Martha Larson (Radboud University)

Read More

Empirical Scanning Analysis of Censys and Shodan

Christopher Bennett, AbdelRahman Abdou, and Paul C. van Oorschot (School of Computer Science, Carleton University, Canada)

Read More

Hey Alexa, is this Skill Safe?: Taking a Closer...

Christopher Lentzsch (Ruhr-Universität Bochum), Sheel Jayesh Shah (North Carolina State University), Benjamin Andow (Google), Martin Degeling (Ruhr-Universität Bochum), Anupam Das (North Carolina State University), William Enck (North Carolina State University)

Read More

(Short) Object Removal Attacks on LiDAR-based 3D Object Detectors

Zhongyuan Hau, Kenneth Co, Soteris Demetriou, and Emil Lupu (Imperial College London) Best Short Paper Award Runner-up!

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)