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

Poster: Securing IoT Edge Devices: Applying NIST IR 8259A...

Rahul Choutapally, Konika Reddy Saddikuti, Solomon Berhe (University of the Pacific)

Read More

Characterizing the Impact of Audio Deepfakes in the Presence...

Magdalena Pasternak (University of Florida), Kevin Warren (University of Florida), Daniel Olszewski (University of Florida), Susan Nittrouer (University of Florida), Patrick Traynor (University of Florida), Kevin Butler (University of Florida)

Read More

Automatic Library Fuzzing through API Relation Evolvement

Jiayi Lin (The University of Hong Kong), Qingyu Zhang (The University of Hong Kong), Junzhe Li (The University of Hong Kong), Chenxin Sun (The University of Hong Kong), Hao Zhou (The Hong Kong Polytechnic University), Changhua Luo (The University of Hong Kong), Chenxiong Qian (The University of Hong Kong)

Read More

A Formal Approach to Multi-Layered Privileges for Enclaves

Ganxiang Yang (Shanghai Jiao Tong University), Chenyang Liu (Shanghai Jiao Tong University), Zhen Huang (Shanghai Jiao Tong University), Guoxing Chen (Shanghai Jiao Tong University), Hongfei Fu (Shanghai Jiao Tong University), Yuanyuan Zhang (Shanghai Jiao Tong University), Haojin Zhu (Shanghai Jiao Tong University)

Read More