Learning Generalizable Multimodal Representations for Software Vulnerability Detection
Source code and its accompanying comments are complementary yet naturally aligned modalities-code encodes structural logic while comments capture developer intent. However, existing vulnerability detection methods mostly rely on single-modality code representations, overlooking the complementary semantic information embedded in comments and thus limiting their generalization across complex code structures and logical relationships. To address this, we propose MultiVul, a multimodal contrastive framework that aligns code and comment representations through dual similarity learning and consistency regularization, augmented with diverse code-text pairs to improve robustness. Experiments on w...
| Authors | Zeming Dong, Yuejun Guo, Qiang Hu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 28 April 2026 |
| Last updated at source | 30 April 2026 |
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