oxdev/security-auditor-grpo

TEXT GENERATIONConcurrency Cost:1Model Size:0.5BQuant:BF16Ctx Length:32kPublished:Apr 24, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

The oxdev/security-auditor-grpo is a 0.5 billion parameter smart contract security auditor model, built on Qwen2.5-Coder-0.5B-Instruct and fine-tuned using Group Relative Policy Optimization (GRPO). It specializes in identifying security vulnerabilities in Solidity smart contracts, providing detailed findings including classification, severity, impact, exploit code, and recommended fixes. With a 32,768 token context length, it is optimized for quick triage and analysis of smart contract code.

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Overview

oxdev/security-auditor-grpo is a specialized 0.5 billion parameter smart contract security auditor model, fine-tuned from Qwen2.5-Coder-0.5B-Instruct. It leverages Group Relative Policy Optimization (GRPO) on real-world audit findings to identify vulnerabilities in Solidity smart contracts. The model provides structured audit reports, including vulnerability classification (e.g., reentrancy, access control, oracle manipulation), severity assessment (Critical/High/Medium/Low), detailed descriptions, impact analysis, proof-of-concept exploit code, and recommended fixes.

Key Capabilities

  • Vulnerability Identification: Detects a wide range of smart contract vulnerabilities across categories like Reentrancy, Access Control, Oracle Manipulation, Flash Loan, Overflow/Underflow, Front-running, DoS, Token Issues, Storage, Cross-chain, Liquidation, Signature, Initialization, and Rounding.
  • Structured Audit Findings: Generates comprehensive reports with classification, severity, description, impact, exploit code, and fixes.
  • GRPO Fine-tuning: Trained using Group Relative Policy Optimization on a dataset of synthetic samples, showing significant improvement in format compliance and finding rate.
  • Efficient for Triage: As a 0.5B parameter model with a 32,768 token context length, it's designed for rapid analysis and initial vulnerability assessment.

Important Notes

  • Performance: This is a compact 0.5B model, suitable for quick triage rather than replacing professional audits. Future versions (V2) are planned with training on 50,902 real audit findings for significant quality improvements.
  • Inference Configuration: Users should set use_cache=True when loading the model for inference to avoid substantial slowdowns.