arXiv AI By Wenxin Tang, Junliang Liu, Wenbin Li, Jingyu Xiao, Xi Xiao, Mingzhe Liu, Jinlong Yang, Xuan Liu, Yuehe Ma, Wang Luo, Qing Li, Lei Wang, Peng Xiangli

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

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DCVD (Dual-Channel Cross-Modal Vulnerability Detection) is a unified framework that jointly performs function-level vulnerability detection and statement-level localization. It extracts control-dependency and semantic features via two parallel branches and fuses them using contrastive alignment and bidirectional cross-attention. The model incorporates explicit supervision at both granularities, achieving superior performance on a large real-world vulnerability benchmark compared to state-of-the-art methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 25

Detecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning

The paper introduces CodeScan, a black-box, vulnerability-oriented scanning framework designed to detect data poisoning and backdoor attacks in code generation large language models (LLMs). CodeScan operates by analyzing structural similarities across multiple code generations, normalizing them with abstract syntax tree (AST) techniques, and then applying LLM-based vulnerability analysis to identify recurring insecure patterns. Evaluations on 117 models across three architectures and multiple sizes show over 97% detection accuracy with fewer false positives compared to prior methods.

By Shenao Yan, Shan Jin, Shimaa Ahmed, Sunpreet Singh Arora, Yiwei Cai, Yizhen Wang, Yuan Hong
arXiv AI
Aug 5

AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection

arXiv:2601. 19138v2 Announce Type: replace-cross Abstract: Secure code review is critical during pre-integration, where Atlassian developers rely on lightweight analysis tools, while deep security assessment is deferred to later stages, delaying feedback and increasing remediation costs.

By Wachiraphan Charoenwet, Kla Tantithamthavorn, Patanamon Thongtanunam, Hong Yi Lin, Minwoo Jeong, Ming Wu