oxdev/security-auditor-grpo
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=Truewhen loading the model for inference to avoid substantial slowdowns.