Decoupled Smart Contract Audits: Lightweight LLM Framework via Distillation and Aggregation

2026-06-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Blockchain & Distributed Ledger Technology · Depth: Expert, quick

Summary

A new efficient end-to-end smart contract security audit framework has been introduced, leveraging lightweight open-source LLMs ranging from 0.6B to 4B parameters. This framework addresses critical security challenges in decentralized web services by decoupling comprehensive audit tasks into four components: vulnerability detection, explanation, severity classification, and remediation recommendation. To maintain high accuracy with fewer parameters, it integrates Rank-Stabilized Low-Rank Adapters (rsLoRA), knowledge distillation, and a custom Chain-of-Verification (CoVe) aggregation strategy. Experimental results show this lightweight pipeline consistently outperforms leading open-source coder dense LLMs (7B to 34B parameters), achieving 98.25% accuracy in vulnerability detection and a 0.4375 alignment score in generative explanation tasks. Ablation studies further validate the decoupled audit processes and identify a novel severity centrality bias, establishing a benchmark for future LLM-assisted auditing research.

Key takeaway

For AI Security Engineers or Smart Contract Developers implementing automated auditing, this framework demonstrates that you can achieve superior vulnerability detection and explanation with lightweight LLMs (0.6B-4B parameters) rather than relying solely on larger models. You should consider decoupling audit tasks into detection, explanation, severity, and remediation, and integrate techniques like rsLoRA and Chain-of-Verification. This approach offers high accuracy (98.25% detection) while significantly reducing computational overhead, making advanced LLM-powered security accessible for your projects.

Key insights

Lightweight LLMs can achieve high accuracy in smart contract auditing by decoupling tasks and employing distillation and aggregation strategies.

Principles

• Decoupling complex audit tasks enhances performance over unified prompting.
• Lightweight LLMs can surpass larger models with targeted optimization.
• A novel severity centrality bias impacts LLM-assisted auditing.

Method

The framework employs Rank-Stabilized Low-Rank Adapters (rsLoRA), knowledge distillation, and a Chain-of-Verification (CoVe) aggregation strategy to process and consolidate responses from lightweight LLMs for audit reports.

In practice

• Apply rsLoRA for efficient fine-tuning of smaller LLMs.
• Implement Chain-of-Verification for robust response aggregation.
• Decompose complex security audits into distinct sub-tasks.

Topics

• Smart Contract Security
• LLM Auditing Frameworks
• Knowledge Distillation
• Low-Rank Adapters
• Vulnerability Detection
• Chain-of-Verification

Best for: Research Scientist, AI Scientist, AI Security Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.