标题： SAEL：利用自适应专家混合的大语言模型进行智能合约漏洞检测 显示英文标题

标题： SAEL: Leveraging Large Language Models with Adaptive Mixture-of-Experts for Smart Contract Vulnerability Detection

摘要： 随着区块链中安全问题的增加，智能合约漏洞检测已成为研究重点。 现有的漏洞检测方法各有其局限性：1）静态分析方法在复杂场景中表现不佳。 2）基于专用预训练模型的方法在特定数据集上表现良好，但泛化能力有限。 相比之下，通用大语言模型（LLMs）在适应新的漏洞模式方面表现出色。 然而，与基于专用预训练模型的方法相比，它们在特定漏洞类型上的表现通常较差。 我们还发现，通用LLMs生成的解释可以提供细粒度的代码理解信息，有助于提高检测性能。 受这些观察的启发，我们提出了SAEL，一种基于LLM的智能合约漏洞检测框架。 我们首先设计了针对性的提示，引导LLMs识别漏洞并生成解释，这些解释作为预测特征。 接下来，我们在CodeT5和T5上应用提示调优，处理合约代码和解释，提升任务特定性能。 为了结合各种方法的优势，我们引入了一种自适应专家混合架构。 该架构通过门控网络动态调整特征权重，使用TopK过滤和Softmax归一化选择相关特征，并引入多头自注意力机制以增强跨特征关系。 这种设计通过梯度优化实现了LLM预测、解释特征和代码特征的有效集成。 损失函数同时考虑独立特征性能和整体加权预测。 实验表明，SAEL在多种漏洞上优于现有方法。 显示英文摘要

摘要： With the increasing security issues in blockchain, smart contract vulnerability detection has become a research focus. Existing vulnerability detection methods have their limitations: 1) Static analysis methods struggle with complex scenarios. 2) Methods based on specialized pre-trained models perform well on specific datasets but have limited generalization capabilities. In contrast, general-purpose Large Language Models (LLMs) demonstrate impressive ability in adapting to new vulnerability patterns. However, they often underperform on specific vulnerability types compared to methods based on specialized pre-trained models. We also observe that explanations generated by general-purpose LLMs can provide fine-grained code understanding information, contributing to improved detection performance. Inspired by these observations, we propose SAEL, an LLM-based framework for smart contract vulnerability detection. We first design targeted prompts to guide LLMs in identifying vulnerabilities and generating explanations, which serve as prediction features. Next, we apply prompt-tuning on CodeT5 and T5 to process contract code and explanations, enhancing task-specific performance. To combine the strengths of each approach, we introduce an Adaptive Mixture-of-Experts architecture. This dynamically adjusts feature weights via a Gating Network, which selects relevant features using TopK filtering and Softmax normalization, and incorporates a Multi-Head Self-Attention mechanism to enhance cross-feature relationships. This design enables effective integration of LLM predictions, explanation features, and code features through gradient optimization. The loss function jointly considers both independent feature performance and overall weighted predictions. Experiments show that SAEL outperforms existing methods across various vulnerabilities.