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

• URL: http://arxiv.org/abs/2507.22371v1
• Date: Wed, 30 Jul 2025 04:28:00 GMT
• Title: SAEL: Leveraging Large Language Models with Adaptive Mixture-of-Experts for Smart Contract Vulnerability Detection
• Authors: Lei Yu, Shiqi Cheng, Zhirong Huang, Jingyuan Zhang, Chenjie Shen, Junyi Lu, Li Yang, Fengjun Zhang, Jiajia Ma,
• Abstract summary: We propose SAEL, an LLM-based framework for smart contract vulnerability detection.<n>We first design targeted prompts to guide LLMs in identifying vulnerabilities and generating explanations.<n>Next, we apply prompt-tuning on CodeT5 and T5 to process contract code and explanations, enhancing task-specific performance.
• Score: 14.581402965011117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• LLAMA: Multi-Feedback Smart Contract Fuzzing Framework with LLM-Guided Seed Generation [56.84049855266145]
  We propose a Multi-feedback Smart Contract Fuzzing framework (LLAMA) that integrates evolutionary mutation strategies, and hybrid testing techniques.<n>LLAMA achieves 91% instruction coverage and 90% branch coverage, while detecting 132 out of 148 known vulnerabilities.<n>These results highlight LLAMA's effectiveness, adaptability, and practicality in real-world smart contract security testing scenarios.
  arXiv  Detail & Related papers  (2025-07-16T09:46:58Z)
• White-Basilisk: A Hybrid Model for Code Vulnerability Detection [50.49233187721795]
  We introduce White-Basilisk, a novel approach to vulnerability detection that demonstrates superior performance.<n>White-Basilisk achieves results in vulnerability detection tasks with a parameter count of only 200M.<n>This research establishes new benchmarks in code security and provides empirical evidence that compact, efficiently designed models can outperform larger counterparts in specialized tasks.
  arXiv  Detail & Related papers  (2025-07-11T12:39:25Z)
• Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection [15.694744168599055]
  Existing vulnerability detection methods face two main issues.<n>They lack comprehensive coverage and high-quality explanations for preference learning.<n>Large language models (LLMs) often struggle with accurately interpreting specific concepts in smart contract security.
  arXiv  Detail & Related papers  (2025-06-23T02:24:07Z)
• Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders [50.52694757593443]
  Existing SAE training algorithms often lack rigorous mathematical guarantees and suffer from practical limitations.<n>We first propose a novel statistical framework for the feature recovery problem, which includes a new notion of feature identifiability.<n>We introduce a new SAE training algorithm based on bias adaptation'', a technique that adaptively adjusts neural network bias parameters to ensure appropriate activation sparsity.
  arXiv  Detail & Related papers  (2025-06-16T20:58:05Z)
• MOS: Towards Effective Smart Contract Vulnerability Detection through Mixture-of-Experts Tuning of Large Language Models [16.16186929130931]
  Smart contract vulnerabilities pose significant security risks to blockchain systems.<n>We propose a smart contract vulnerability detection framework based on mixture-of-experts tuning (MOE-Tuning) of large language models.<n> Experiments show that MOS significantly outperforms existing methods with average improvements of 6.32% in F1 score and 4.80% in accuracy.
  arXiv  Detail & Related papers  (2025-04-16T16:33:53Z)
• Smart-LLaMA: Two-Stage Post-Training of Large Language Models for Smart Contract Vulnerability Detection and Explanation [21.39496709865097]
  Existing smart contract vulnerability detection methods face three main issues. Insufficient quality of datasets, lacking detailed explanations and precise vulnerability locations. We propose Smart-LLaMA, an advanced detection method based on the LLaMA language model.
  arXiv  Detail & Related papers  (2024-11-09T15:49:42Z)
• Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models [8.167614500821223]
  We introduce MSIVD, multitask self-instructed fine-tuning for vulnerability detection, inspired by chain-of-thought prompting and LLM self-instruction. Our experiments demonstrate that MSIVD achieves superior performance, outperforming the highest LLM-based vulnerability detector baseline (LineVul) with a F1 score of 0.92 on the BigVul dataset, and 0.48 on the PreciseBugs dataset.
  arXiv  Detail & Related papers  (2024-06-09T19:18:05Z)
• An Empirical Study of Automated Vulnerability Localization with Large Language Models [21.84971967029474]
  Large Language Models (LLMs) have shown potential in various domains, yet their effectiveness in vulnerability localization remains underexplored. Our investigation encompasses 10+ leading LLMs suitable for code analysis, including ChatGPT and various open-source models. We explore the efficacy of these LLMs using 4 distinct paradigms: zero-shot learning, one-shot learning, discriminative fine-tuning, and generative fine-tuning.
  arXiv  Detail & Related papers  (2024-03-30T08:42:10Z)
• Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information [67.78183175605761]
  Large Language Models are susceptible to adversarial prompt attacks. This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs. We introduce a novel approach to detecting adversarial prompts at a token level.
  arXiv  Detail & Related papers  (2023-11-20T03:17:21Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Evaluating and Explaining Large Language Models for Code Using Syntactic Structures [74.93762031957883]
  This paper introduces ASTxplainer, an explainability method specific to Large Language Models for code. At its core, ASTxplainer provides an automated method for aligning token predictions with AST nodes. We perform an empirical evaluation on 12 popular LLMs for code using a curated dataset of the most popular GitHub projects.
  arXiv  Detail & Related papers  (2023-08-07T18:50:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.