arXiv cs.AI· Wenxin Tang, Junliang Liu, Wenbin Li, Jingyu Xiao, Xi Xiao, Mingzhe Liu, Jinlong Yang, Xuan Liu, Yuehe Ma, Wang Luo, Qing Li, Lei Wang, Peng Xiangli·· 9 天前AI 评分22
DCVD：双通道跨模态融合实现漏洞检测与定位联合建模
DCVD: Dual-Channel Cross-Modal Fusion for Joint Vulnerability Detection and Localization
AI 导读
DCVD 是一个双通道跨模态漏洞检测框架，通过控制依赖与语义两条并行分支提取特征，并用对比对齐与双向交叉注意力融合，实现函数级检测与语句级定位的联合优化。在大型真实漏洞基准上，DCVD 在函数级检测和语句级定位两项任务上均持续优于现有 SOTA 方法，代码已开源。
正文 · 原文
Authors:Wenxin Tang, Junliang Liu, Wenbin Li, Jingyu Xiao, Xi Xiao, Mingzhe Liu, Jinlong Yang, Xuan Liu, Yuehe Ma, Wang Luo, Qing Li, Lei Wang, Peng Xiangli
Abstract:Software vulnerability detection plays a critical role in ensuring system security, where real-world auditing requires not only determining whether a function is vulnerable but also pinpointing the specific lines responsible. However, existing approaches either rely on a single information source -- sequential, structural, or semantic -- failing to jointly exploit the complementary strengths across modalities, or treat statement-level localization merely as a byproduct of function-level detection without explicit line-level supervision. To address these limitations, we propose DCVD (Dual-Channel Cross-Modal Vulnerability Detection), a unified framework that performs joint function-level detection and statement-level localization. DCVD extracts control-dependency and semantic features through two parallel branches and integrates them via contrastive alignment coupled with bidirectional cross-attention, effectively bridging the cross-modal representation gap. It further introduces explicit supervision signals at both the function and statement levels, enabling collaborative optimization across the two granularities. Extensive experiments on a large-scale real-world vulnerability benchmark demonstrate that DCVD consistently outperforms state-of-the-art methods on both function-level detection and statement-level localization. Our code is available at this https URL.
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.11015 [cs.CR] |
| (or arXiv:2605.11015v2 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.11015 arXiv-issued DOI via DataCite |
Submission history
From: Wenxin Tang [view email]
[v1]
Sun, 10 May 2026 14:33:25 UTC (249 KB)
[v2]
Thu, 8 Oct 2026 14:12:28 UTC (249 KB)
来源：arXiv cs.AI · arxiv.org