DCVD: Dual-Channel Cross-Modal Fusion for Joint Vulnerability Detection and Localization

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.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

arXiv:2605.11015 [cs.CR]

(or arXiv:2605.11015v2 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2605.11015 Focus to learn more arXiv-issued DOI via DataCite

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