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Litevul: a lightweight vulnerability detection via directed PPMI-weighted token graphs.

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Bibliographic Details
Title: Litevul: a lightweight vulnerability detection via directed PPMI-weighted token graphs.
Authors: Liu, Zhaohui, Xie, Wenjie
Source: Cybersecurity (2523-3246); 3/18/2026, Vol. 9 Issue 1, p1-20, 20p
Subject Terms: SOURCE code, GRAPH neural networks, COMPUTER software security, GRAPH theory, PENETRATION testing (Computer security)
Abstract: Source code vulnerability detection is critical to software security. Existing methods either rely on external tools to construct program graphs, which are constrained by environmental limitations, incur high computational costs, and are prone to failure, or adopt lightweight sequence models that overlook code structural features. To address this, we propose LiteVul, a lightweight framework that constructs compact directed graphs directly from raw source code without relying on external tools, capturing multi-scale lexical co-occurrence relationships and fine-grained structural dependencies. By leveraging structure-aware graph neural networks, LiteVul aggregates multi-layer features to achieve precise vulnerability prediction. Experiments conducted on the Devign, Reveal, and DiverseVul benchmark datasets show that LiteVul substantially surpasses nine representative baseline methods in performance. Additionally, its graph construction efficiency is substantially improved, providing a feasible technical pathway for vulnerability detection in large-scale codebases. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Source code vulnerability detection is critical to software security. Existing methods either rely on external tools to construct program graphs, which are constrained by environmental limitations, incur high computational costs, and are prone to failure, or adopt lightweight sequence models that overlook code structural features. To address this, we propose LiteVul, a lightweight framework that constructs compact directed graphs directly from raw source code without relying on external tools, capturing multi-scale lexical co-occurrence relationships and fine-grained structural dependencies. By leveraging structure-aware graph neural networks, LiteVul aggregates multi-layer features to achieve precise vulnerability prediction. Experiments conducted on the Devign, Reveal, and DiverseVul benchmark datasets show that LiteVul substantially surpasses nine representative baseline methods in performance. Additionally, its graph construction efficiency is substantially improved, providing a feasible technical pathway for vulnerability detection in large-scale codebases. [ABSTRACT FROM AUTHOR]
ISSN:25233246
DOI:10.1186/s42400-026-00570-x