During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which causes inaccuracy for existing methods. In this paper, we propose a new approach named \textbf{\textit{PS}$^3$}, referring to \emph{precise patch presence} test based on \emph{semantic-level symbolic signature}. \textbf{\textit{PS}$^3$} exploits symbolic emulation to extract signatures that are stable under different compiler options. Then \textbf{\textit{PS}$^3$} can precisely test the presence of the patch by comparing the signatures between the reference and the target at semantic level. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 3,631 (CVE, binary) pairs of 62 recent CVEs in four C/C++ projects. The experimental results show that \textbf{\textit{PS}$^3$} achieves scores of 0.82, 0.97, and 0.89 in terms of precision, recall, and F1 score, respectively. \textbf{\textit{PS}$^3$} outperforms the state-of-the-art baselines by improving 33\% in terms of F1 score and remains stable in different compiler options.

翻译：在软件开发过程中，漏洞对用户构成了重大威胁。补丁是应对漏洞最有效的方式。在大规模软件系统中，测试每个受影响二进制文件中安全补丁的存在性对于确保系统安全至关重要。由于已修补版本和易受攻击版本之间可能仅存在微小差异，因此判断二进制文件是否已针对已知漏洞进行修补极具挑战性。现有方法主要侧重于检测在相同编译器选项下编译的补丁。然而，开发者在不同情况下常使用差异显著的编译器选项编译程序，这导致现有方法存在不准确性。本文提出一种名为\textbf{\textit{PS}$^3$}的新方法，即基于语义级别符号签名进行精确补丁存在性测试。\textbf{\textit{PS}$^3$}利用符号模拟提取在不同编译器选项下保持稳定的签名，然后通过比较参考版本与目标版本在语义级别上的签名，精确测试补丁的存在性。为评估方法的有效性，我们构建了一个包含4个C/C++项目中62个近期CVE的3,631对（CVE,二进制文件）数据集。实验结果表明：\textbf{\textit{PS}$^3$}在精确率、召回率和F1分数上分别达到0.82、0.97和0.89。相比现有最先进基线方法，\textbf{\textit{PS}$^3$}将F1分数提升33%，且在不同编译器选项下保持稳定。