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

PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented...

Ye Liu (Singapore Management University), Yue Xue (MetaTrust Labs), Daoyuan Wu (The Hong Kong University of Science and Technology), Yuqiang Sun (Nanyang Technological University), Yi Li (Nanyang Technological University), Miaolei Shi (MetaTrust Labs), Yang Liu (Nanyang Technological University)

Read More

SongBsAb: A Dual Prevention Approach against Singing Voice Conversion...

Guangke Chen (Pengcheng Laboratory), Yedi Zhang (National University of Singapore), Fu Song (Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Science; Nanjing Institute of Software Technology), Ting Wang (Stony Brook University), Xiaoning Du (Monash University), Yang Liu (Nanyang Technological University)

Read More

Probe-Me-Not: Protecting Pre-trained Encoders from Malicious Probing

Ruyi Ding (Northeastern University), Tong Zhou (Northeastern University), Lili Su (Northeastern University), Aidong Adam Ding (Northeastern University), Xiaolin Xu (Northeastern University), Yunsi Fei (Northeastern University)

Read More