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

Unleashing the Power of Generative Model in Recovering Variable...

Xiangzhe Xu (Purdue University), Zhuo Zhang (Purdue University), Zian Su (Purdue University), Ziyang Huang (Purdue University), Shiwei Feng (Purdue University), Yapeng Ye (Purdue University), Nan Jiang (Purdue University), Danning Xie (Purdue University), Siyuan Cheng (Purdue University), Lin Tan (Purdue University), Xiangyu Zhang (Purdue University)

Read More

IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systems

Yuhao Wu (Washington University in St. Louis), Franziska Roesner (University of Washington), Tadayoshi Kohno (University of Washington), Ning Zhang (Washington University in St. Louis), Umar Iqbal (Washington University in St. Louis)

Read More

Siniel: Distributed Privacy-Preserving zkSNARK

Yunbo Yang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Yuejia Cheng (Shanghai DeCareer Consulting Co., Ltd), Kailun Wang (Beijing Jiaotong University), Xiaoguo Li (College of Computer Science, Chongqing University), Jianfei Sun (School of Computing and Information Systems, Singapore Management University), Jiachen Shen (Shanghai Key Laboratory of Trustworthy Computing, East China Normal…

Read More

Compiled Models, Built-In Exploits: Uncovering Pervasive Bit-Flip Attack Surfaces...

Yanzuo Chen (The Hong Kong University of Science and Technology), Zhibo Liu (The Hong Kong University of Science and Technology), Yuanyuan Yuan (The Hong Kong University of Science and Technology), Sihang Hu (Huawei Technologies), Tianxiang Li (Huawei Technologies), Shuai Wang (The Hong Kong University of Science and Technology)

Read More