200M参数模型实现顶尖漏洞检测，突破大模型依赖
White-Basilisk: A Hybrid Model for Code Vulnerability Detection
- 融合Mamba、线性注意力与专家混合架构，高效处理超长代码
- 仅200M参数达成领先性能，单次扫描可分析完整代码库
- 适合需轻量高效部署的工业级安全团队使用
软件漏洞泛滥给网络安全带来严峻挑战，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，在挑战现有AI模型扩展假设的同时展现出卓越性能。该模型采用创新架构，结合Mamba层、线性自注意力和专家混合（Mixture of Experts）框架，仅用200M参数即在漏洞检测任务中达到业界领先水平。其处理前所未有的长序列能力，使单次遍历即可完成大规模代码库的全面分析，突破当前大语言模型（LLMs）的上下文限制。White-Basilisk在不平衡的真实数据集上表现稳健，同时保持计算效率，支持跨不同规模组织的部署。本研究不仅确立了代码安全新基准，还提供了实证依据：对于特定任务，精巧设计的小型模型可超越大型模型，或重新定义领域专用AI开发的优化策略。
原文摘要 · Abstract (English)
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.
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