App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 2

Cross-modal vulnerability detection model based on consistency verification and adaptive gating

Lu Shize1
Huang Jiangtao1,2
Wang Siliang3
1. School of Artificial Intelligence, Nanning Normal University, Nanning 530100, China
2. Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Nanning 530100, China
3. School of Rail Transit, Chengdu Vocational & Technical College of Industry, Chengdu 610218, China
DOI: 10.19734/j.issn.1001-3695.2026.05.0179

Abstract

This paper proposed a cross-modal vulnerability detection model based on consistency verification and adaptive gating (GateVul) . This model addressed the trade-off between deep reasoning gains and hallucination noise interference in Large Language Model (LLM) -assisted vulnerability detection. The model used deep prompt engineering to guide the LLM in extracting deep security logic and endogenous confidence from the code. It constructed a three-dimensional semantic decision space consisting of semantic representation, endogenous confidence, and explicit conflict signals. An adaptive gating mechanism dynamically evaluated the credibility of the code explanation modality to block the injection of hallucination features. Experimental results on three public datasets show that GateVul significantly outperforms existing baseline models in detection performance, and demonstrates strong robustness when handling imbalanced and high-noise data. Ablation experiments confirm the effectiveness of this fusion strategy in suppressing hallucination noise.

Foundation Support

国家自然科学基金资助项目(62067007)
广西科技创新平台计划项目(桂科LT2600640001)
广西重点研发计划项目(桂科AB21076009)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.05.0179
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 2

Publish History

[2026-10-23] Accepted Paper

Cite This Article

陆世泽, 黄江涛, 王斯梁. 基于一致性校验与自适应门控的跨模态漏洞检测模型 [J]. 计算机应用研究, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0179. (Lu Shize, Huang Jiangtao, Wang Siliang. Cross-modal vulnerability detection model based on consistency verification and adaptive gating [J]. Application Research of Computers, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.05.0179. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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