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ARXIV:2606.03128 · SMART CONTRACT AUDITING · SUBMITTED 03 JUN · 20:41 UTC · FRESHNESS FRESH
ARXIV:2606.03128SMART CONTRACT AUDITINGSUBMITTED 03 JUN · 20:41 UTCFRESHNESS FRESHBagus Rakadyanto Oktavianto Putra · Muhamad Risqi Utama Saputra · Widyawan · Guntur Dharma Putra · arXiv
A lightweight LLM framework for smart contract auditing that decouples tasks, uses distillation and aggregation for high accuracy with minimal parameters.
Opportunity summary
Pain A lightweight LLM framework for smart contract auditing that decouples tasks, uses distillation and aggregation for high accuracy with minimal parameters.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A lightweight LLM framework for smart contract auditing that decouples tasks, uses distillation and aggregation for high accuracy with minimal parameters. While Large Language Models (LLMs) have shown promise in automated vulnerability detection, existing…
Smart contracts face critical security challenges that require thorough auditing in decentralized web services. While Large Language Models (LLMs) have shown promise in automated vulnerability detection, existing approaches lack severity evaluations with actionable remediation…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Experimental results demonstrate that our lightweight pipeline consistently outperforms state-of-the-art open-source coder dense LLMs (7B to 34B parameters), achieving 98.25% accuracy in vulnerability detection…
Smart Contract Auditing moved forward this cycle; last verified June 2026. Public score 8.0/10. Production flags indicate code availability.
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A lightweight LLM framework for smart contract auditing that decouples tasks, uses distillation and aggregation for high accuracy with minimal parameters.
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10.48550/arXiv.2606.03128A lightweight LLM framework for smart contract auditing that decouples tasks, uses distillation and aggregation for high accuracy with minimal parameters.
Abstract
Smart contracts face critical security challenges that require thorough auditing in decentralized web services. While Large Language Models (LLMs) have shown promise in automated vulnerability detection, existing approaches lack severity evaluations with actionable remediation and demand unnecessarily massive computational overhead. In this study, we introduce an efficient end-to-end smart contract security audit framework utilizing lightweight, highly optimized open-source LLMs (0.6B-4B parameters). Our framework decouples comprehensive audit tasks into four interconnected components: vulnerability detection, explanation, severity classification, and remediation recommendation. To maintain high accuracy without massive parameters, we implement Rank-Stabilized Low-Rank Adapters (rsLoRA), knowledge distillation, and a custom Chain-of-Verification (CoVe) aggregation strategy to systematically screen and consolidate multiple draft responses from the model into a highly accurate audit report. Experimental results demonstrate that our lightweight pipeline consistently outperforms state-of-the-art open-source coder dense LLMs (7B to 34B parameters), achieving 98.25% accuracy in vulnerability detection and an alignment score of 0.4375 in generative explanation tasks. Furthermore, our extensive ablation studies empirically validate the superiority of our decoupled audit processes over unified prompting and uncover a novel severity centrality bias, establishing a critical benchmark for future research in LLM-assisted auditing.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 8.0
PROBLEM
A lightweight LLM framework for smart contract auditing that decouples tasks, uses distillation and aggregation for high accuracy with minimal parameters. While Large Language Models (LLMs) have shown promise in automated vulnerability detection, existing approaches lack severit...
METHOD
Smart contracts face critical security challenges that require thorough auditing in decentralized web services. While Large Language Models (LLMs) have shown promise in automated vulnerability detection, existing approaches lack severity evaluations with actionable remediation a...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Experimental results demonstrate that our lightweight pipeline consistently outperforms state-of-the-art open-source coder dense LLMs (7B to 34B parameters), achieving 98.25% accuracy in vulnerability det...
WHY NOW
Smart Contract Auditing moved forward this cycle; last verified June 2026. Public score 8.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 12, "author": "Bagus Rakadyanto Oktavianto Putra; Muhamad Risqi Utama Saputra; Widyawan; Guntur Dharma Putra"
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Concepts
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A lightweight LLM framework for smart contract auditing that decouples tasks, uses distillation and aggregation for high accuracy with minimal parameters.
Segment
Smart Contract Auditing
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Commercial read
8.0/10 public viability
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