标题： 白-巴尔萨蜥蜴：一种代码漏洞检测的混合模型 显示英文标题

标题： White-Basilisk: A Hybrid Model for Code Vulnerability Detection

摘要： 软件漏洞的激增给网络安全带来了重大挑战，需要更有效的检测方法。我们引入了White-Basilisk，这是一种新颖的漏洞检测方法，在挑战现有AI模型扩展假设的同时表现出卓越的性能。利用一种创新的架构，结合Mamba层、线性自注意机制和专家混合框架，White-Basilisk在仅具有2亿参数的情况下，在漏洞检测任务中达到了最先进的结果。该模型处理前所未有的长序列的能力，使得在单次遍历中能够全面分析大型代码库，超过了当前大型语言模型（LLMs）的上下文限制。White-Basilisk在不平衡的真实数据集上表现出强大的性能，同时保持计算效率，便于在不同规模的组织中部署。这项研究不仅在代码安全领域设定了新的基准，还提供了实证证据，表明紧凑且高效设计的模型可以在专业任务中超越更大的模型，可能重新定义针对特定应用的AI开发中的优化策略。 显示英文摘要

摘要： 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.