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

arXiv: https://arxiv.org/abs/2605.11015 · HTML: https://mlsanity.com/paper/2605.11015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 10 May 2026.

## Builds on (most cited first, 19 of 19)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [Vulnerability Detection with Fine-grained Interpretations](https://mlsanity.com/paper/2106.10478/) (2021)
- [VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection](https://mlsanity.com/paper/2205.12424/) (2022)
- [Interaction2Code: Benchmarking MLLM-based Interactive Webpage Code Generation from Interactive Prototyping](https://mlsanity.com/paper/2411.03292/) (2024)
- [LineVD: Statement-level Vulnerability Detection using Graph Neural Networks](https://mlsanity.com/paper/2203.05181/) (2022)
- [Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning](https://mlsanity.com/paper/2406.03718/) (2024)
- [SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design](https://mlsanity.com/paper/2506.07964/) (2025)
- [VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements](https://mlsanity.com/paper/2112.10893/) (2021)
- [Pre-training by Predicting Program Dependencies for Vulnerability Analysis Tasks](https://mlsanity.com/paper/2402.00657/) (2024)
- [SCALE: Constructing Structured Natural Language Comment Trees for Software Vulnerability Detection](https://mlsanity.com/paper/2403.19096/) (2024)
- [Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2406.05892/) (2024)
- [EfficientPosterGen: Semantic-aware Efficient Poster Generation via Token Compression and Accurate Violation Detection](https://mlsanity.com/paper/2603.00155/) (2026)
- [When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection](https://mlsanity.com/paper/2308.10523/) (2023)
- [Benchmarking MLLM-based Web Understanding: Reasoning, Robustness and Safety](https://mlsanity.com/paper/2509.21782/) (2025)
- [DFEPT: Data Flow Embedding for Enhancing Pre-Trained Model Based Vulnerability Detection](https://mlsanity.com/paper/2410.18479/) (2024)
- [Rethinking Multimodal Point Cloud Completion: A Completion-by-Correction Perspective](https://mlsanity.com/paper/2511.12170/) (2025)

## Abstract (arXiv)

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 https://github.com/vinsontang1/DCVD.