English(EN) VulDec: Vulnerability Detection in Binaries via Neural Decompilation

新的VulDec框架使用神经反编译进行二进制漏洞检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员推出了一种新颖的VulDec框架，该框架通过采用神经反编译来检测二进制可执行文件中的漏洞。这种方法利用大型语言模型从汇编代码中恢复源代码级别的代码，从而解决了在没有源代码访问权限的情况下分析代码的挑战。该框架包含一个新数据集，该数据集在各种架构上将汇编-C函数对与通用弱点枚举（CWE）注释对齐。VulDec的神经反编译程序实现了显著的源代码级别恢复，其检测器在真实测试数据上达到了0.61的宏观F1分数。 AI

影响通过在没有源代码访问权限的情况下对编译后的代码进行漏洞检测，增强了自动化安全分析。

排序理由详细介绍一种新的漏洞检测方法和数据集的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Francesco Crescenzo Grasso, Pietro Liguori, Roberto Natella, Domenico Cotroneo · 2026-10-28 04:00

VulDec：通过神经反编译在二进制文件中检测漏洞

arXiv:2610.33716v1 Announce Type: cross Abstract: Automated vulnerability detection in binary executables is challenging due to the lack of source code and the limited semantic information available at the assembly level. Despite significant progress in machine-learning-based cod…

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VulDec：通过神经反编译在二进制文件中检测漏洞

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