# SAEL: Leveraging Large Language Models with Adaptive Mixture-of-Experts for Smart Contract Vulnerability Detection arXiv: https://arxiv.org/abs/2507.22371 ยท HTML: https://mlsanity.com/paper/2507.22371/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in Expert routing strategies, one of 12 directions in Mixture-of-experts. - Builds on 28 of its 51 references that are in the map. - First posted on arXiv 30 Jul 2025. ## Placed in - Mixture-of-experts / [Expert routing strategies](https://mlsanity.com/moe/expert-routing-strategies/) ## Builds on (most cited first, 28 of 28) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019) - [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020) - [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021) - [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020) - [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019) - [GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis](https://mlsanity.com/paper/2308.03314/) (2023) - [Securify: Practical Security Analysis of Smart Contracts](https://mlsanity.com/paper/1806.01143/) (2018) - [Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives](https://mlsanity.com/paper/2310.01152/) (2023) - [LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2308.11148/) (2023) - [Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study](https://mlsanity.com/paper/2309.08221/) (2023) - [When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?](https://mlsanity.com/paper/2309.05520/) (2023) - [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024) - [Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts](https://mlsanity.com/paper/1907.03890/) (2019) - [Do you still need a manual smart contract audit?](https://mlsanity.com/paper/2306.12338/) (2023) - [SWE-bench-java: A GitHub Issue Resolving Benchmark for Java](https://mlsanity.com/paper/2408.14354/) (2024) - [A Survey on Ethereum Systems Security: Vulnerabilities, Attacks and Defenses](https://mlsanity.com/paper/1908.04507/) (2019) - [Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion](https://mlsanity.com/paper/2106.09282/) (2021) - [SmartBugs: A Framework to Analyze Solidity Smart Contracts](https://mlsanity.com/paper/2007.04771/) (2020) - [DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation](https://mlsanity.com/paper/2412.18291/) (2024) - [Rethinking Smart Contract Fuzzing: Fuzzing With Invocation Ordering and Important Branch Revisiting](https://mlsanity.com/paper/2301.03943/) (2023) - [Smart-LLaMA: Two-Stage Post-Training of Large Language Models for Smart Contract Vulnerability Detection and Explanation](https://mlsanity.com/paper/2411.06221/) (2024) - [Security Analysis Methods on Ethereum Smart Contract Vulnerabilities: A Survey](https://mlsanity.com/paper/1908.08605/) (2019) - [Improving Smart Contract Security with Contrastive Learning-based Vulnerability Detection](https://mlsanity.com/paper/2404.17839/) (2024) - [Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2506.18245/) (2025) - [Blockchain-based Smart Contracts: A Systematic Mapping Study](https://mlsanity.com/paper/1710.06372/) (2017) - [EASYFLOW: Keep Ethereum Away From Overflow](https://mlsanity.com/paper/1811.03814/) (2018) - [MOS: Towards Effective Smart Contract Vulnerability Detection through Mixture-of-Experts Tuning of Large Language Models](https://mlsanity.com/paper/2504.12234/) (2025) ## Built on by (most cited first, 2 of 2) - [The Rise of Sparse Mixture-of-Experts: A Survey from Algorithmic Foundations to Decentralized Architectures and Vertical Domain Applications](https://mlsanity.com/paper/2602.08019/) (2026) - [Towards Secure and Explainable Smart Contract Generation with Security-Aware Group Relative Policy Optimization](https://mlsanity.com/paper/2509.09942/) (2025) ## Abstract (arXiv) 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.