The proliferation of software vulnerabilities presents a significant challenge to cybersecurity, necessitating more effective detection methodologies. We introduce White-Basilisk, a novel approach to vulnerability detection that demonstrates superior performance while challenging prevailing assumptions in AI model scaling. Utilizing an innovative 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 sequences of unprecedented length enables comprehensive analysis of extensive codebases in a single pass, surpassing the context limitations of current Large Language Models (LLMs). White-Basilisk exhibits robust performance on imbalanced, real-world datasets, while maintaining computational efficiency that facilitates deployment across diverse organizational scales. This research not only establishes new benchmarks in code security but also provides empirical evidence that compact, efficiently designed models can outperform larger counterparts in specialized tasks, potentially redefining optimization strategies in AI development for domain-specific applications.

翻译：软件漏洞的激增对网络安全构成了重大挑战，亟需更有效的检测方法。本文提出White-Basilisk，一种新型漏洞检测方法，它在展现卓越性能的同时，挑战了AI模型扩展领域的既有假设。通过集成Mamba层、线性自注意力和混合专家（MoE）框架的创新架构，White-Basilisk在参数总量仅为2亿的情况下，在漏洞检测任务中取得了最先进的成果。该模型处理超长序列的能力使其能单次全面分析大规模代码库，突破了当前大型语言模型（LLM）的上下文长度限制。White-Basilisk在不平衡的真实世界数据集上展现出稳健性能，同时维持了计算效率，便于在各类组织规模中部署。本研究不仅为代码安全确立了新基准，更提供了经验证据，表明在特定任务中，紧凑且高效设计的模型能够超越规模更大的同类模型，这有望重新定义面向领域特定应用的AI开发优化策略。