Title: White-Basilisk: A Hybrid Model for Code Vulnerability Detection
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: vulnerability detection, llm, linear attention, mamba, white-basilisk, nlp, moe, efficient, source code, c, c++

Abstract: The proliferation of software vulnerabilities presents a significant challenge to cybersecurity, necessitating more effective detection methodologies. We introduce White-Basilisk, a hybrid approach to vulnerability detection that demonstrates strong performance with efficient architectural design. Utilizing an architecture that integrates Mamba layers, linear self-attention, and a Mixture of Experts framework, White-Basilisk achieves state-of-the-art results in vulnerability detection tasks with a parameter count of only 200M. The model's capacity to process extended sequences enables comprehensive analysis of large codebases in a single pass, addressing context limitations that affect current approaches. White-Basilisk exhibits robust performance on imbalanced, real-world datasets, while maintaining computational efficiency that facilitates deployment across diverse organizational scales. This research establishes new benchmarks in code security and provides empirical evidence that compact, efficiently designed models can achieve competitive performance on specialized tasks, contributing to our understanding of architectural efficiency in domain-specific applications.