🔐 Smart Contract Security Auditor (GRPO)

A specialized smart contract security auditor built on Qwen2.5-Coder-0.5B-Instruct, fine-tuned using Group Relative Policy Optimization (GRPO) on real-world audit findings from top security firms.

🎯 What It Does

Given a Solidity smart contract, this model identifies security vulnerabilities and produces structured audit findings with:

• Vulnerability classification (reentrancy, access control, oracle manipulation, etc.)
• Severity assessment (Critical/High/Medium/Low)
• Detailed description of the vulnerability
• Impact analysis
• Proof of concept exploit code
• Recommended fixes

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model = AutoModelForCausalLM.from_pretrained(
    "oxdev/security-auditor-grpo",
    use_cache=True,  # Important: config has use_cache=False from training
)
tokenizer = AutoTokenizer.from_pretrained("oxdev/security-auditor-grpo")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device="cuda")

messages = [
    {"role": "system", "content": "You are an expert smart contract security auditor. Analyze the provided Solidity code for vulnerabilities."},
    {"role": "user", "content": """Audit this contract:
```solidity
contract SimpleBank {
    mapping(address => uint256) public balances;
    function deposit() public payable { balances[msg.sender] += msg.value; }
    function withdraw(uint256 amount) public {
        require(balances[msg.sender] >= amount);
        (bool success, ) = msg.sender.call{value: amount}("");
        require(success);
        balances[msg.sender] -= amount;
    }
}
```"""},
]

result = pipe(messages, max_new_tokens=512, do_sample=False, return_full_text=False)
output = result[0]["generated_text"]
if isinstance(output, list):
    output = output[-1]["content"]
print(output)

🔗 Try It Live

Interactive Demo: oxdev/security-auditor-demo — Side-by-side comparison with base model, 7 test cases with known vulnerabilities, automated scoring.

Training Details

V1 (Current Model)

• Method: GRPO (Group Relative Policy Optimization)
• Base Model: Qwen2.5-Coder-0.5B-Instruct
• Dataset: oxdev/smart-contract-security-sft (327 synthetic samples)
• Hardware: NVIDIA T4 (16GB)
• Epochs: 2
• Reward Functions: Format compliance, finding rate
• Results:
  • Format reward: 0.025 → 0.40 (16× improvement)
  • Finding rate: 0% → 50-75%
  • Mean reward: -0.34 → -0.006

V2 (Pending — Colab Notebook Ready)

• Dataset: oxdev/smart-contract-security-audit-v2 (50,902 real audit findings)
• Sources: SkywardNomad92/smart-contract-audit-findings, samscrack/cyfrin-audit-findings, Solodit API
• 4 Reward Functions: Format (0.25), Severity matching (0.25), Category matching (0.25), Quality (0.25)
• Train on Colab: Open train_grpo_v2_colab.ipynb in Google Colab with a free T4 GPU

Vulnerability Categories Covered

Architecture

• Model: Qwen2ForCausalLM
• Parameters: 0.5B
• Hidden Size: 896
• Layers: 24
• Attention Heads: 14 (2 KV heads)
• Context Length: 32,768 tokens
• Chat Template: ChatML (<|im_start|> / <|im_end|>)

⚠️ Important Notes

1. Set use_cache=True when loading for inference — the saved config has use_cache=False from training, which makes generation 10-20× slower
2. This is a 0.5B model — it's fast but not as capable as larger models. Use it for quick triage, not as a replacement for professional audits
3. V1 was trained on 327 samples — V2 training on 50K real findings will significantly improve quality

Files

Related Resources

• GitHub: 0xedev/skills — Pashov Audit Group AI-powered security skills
• V2 Dataset: oxdev/smart-contract-security-audit-v2
• Demo Space: oxdev/security-auditor-demo

Framework Versions

• TRL: 1.2.0
• Transformers: 5.6.2
• PyTorch: 2.6.0+cu126
• Datasets: 4.8.4

Citations

@article{shao2024deepseekmath,
    title   = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
    author  = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and others},
    year    = 2024,
    eprint  = {arXiv:2402.03300},
}