Hongwei Wu (Purdue University), Jianliang Wu (Simon Fraser University), Ruoyu Wu (Purdue University), Ayushi Sharma (Purdue University), Aravind Machiry (Purdue University), Antonio Bianchi (Purdue University)

Vendors are often provided with updated versions of a piece of software, fixing known security issues.
However, the inability to have any guarantee that the provided patched software does not break the functionality of its original version often hinders patch deployment.
This issue is particularly severe when the patched software is only provided in its compiled binary form.
In this case, manual analysis of the patch's source code is impossible, and existing automated patch analysis techniques, which rely on source code, are not applicable.
Even when the source code is accessible, the necessity of binary-level patch verification is still crucial, as highlighted by the recent XZ Utils backdoor.

To tackle this issue, we propose VeriBin, a system able to compare a binary with its patched version and determine whether the patch is ''Safe to Apply'', meaning it does not introduce any modification that could potentially break the functionality of the original binary.
To achieve this goal, VeriBin checks functional equivalence between the original and patched binaries.
In particular, VeriBin first uses symbolic execution to systematically identify patch-introduced modifications.
Then, it checks if the detected patch-introduced modifications respect specific properties that guarantee they will not break the original binary's functionality.
To work without source code, VeriBin's design solves several challenges related to the absence of semantic information (removed during the compilation process) about the analyzed code and the complexity of symbolically executing large functions precisely.
Our evaluation of VeriBin on a dataset of 86 samples shows that it achieves an accuracy of 93.0% with no false positives, requiring only minimal analyst input.
Additionally, we showcase how VeriBin can be used to detect the recently discovered XZ Utils backdoor.

View More Papers

Starshields for iOS: Navigating the Security Cosmos in Satellite...

Jiska Classen (Hasso Plattner Institute, University of Potsdam), Alexander Heinrich (TU Darmstadt, Germany), Fabian Portner (TU Darmstadt, Germany), Felix Rohrbach (TU Darmstadt, Germany), Matthias Hollick (TU Darmstadt, Germany)

Read More

JBomAudit: Assessing the Landscape, Compliance, and Security Implications of...

Yue Xiao (IBM Research), Dhilung Kirat (IBM Research), Douglas Lee Schales (IBM Research), Jiyong Jang (IBM Research), Luyi Xing (Indiana University Bloomington), Xiaojing Liao (Indiana University)

Read More

Ctrl+Alt+Deceive: Quantifying User Exposure to Online Scams

Platon Kotzias (Norton Research Group, BforeAI), Michalis Pachilakis (Norton Research Group, Computer Science Department University of Crete), Javier Aldana Iuit (Norton Research Group), Juan Caballero (IMDEA Software Institute), Iskander Sanchez-Rola (Norton Research Group), Leyla Bilge (Norton Research Group)

Read More

Reinforcement Unlearning

Dayong Ye (University of Technology Sydney), Tianqing Zhu (City University of Macau), Congcong Zhu (City University of Macau), Derui Wang (CSIRO’s Data61), Kun Gao (University of Technology Sydney), Zewei Shi (CSIRO’s Data61), Sheng Shen (Torrens University Australia), Wanlei Zhou (City University of Macau), Minhui Xue (CSIRO's Data61)

Read More