PolyGuard — Code Vulnerability Scanner

A fine-tuned CodeBERT model for detecting security vulnerabilities in source code.

Supported Languages

Python, JavaScript, SQL, PHP, Java, C, C++, Go, Ruby, Rust

Performance

• F1 Score: 0.6698
• Training samples: 16681
• Base model: microsoft/codebert-base
• Trained at: 2026-04-29

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "MUHAMMADSAADAMIN/PolyGuard"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model     = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

code = "eval(input())"
inputs = tokenizer(code, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    logits = model(**inputs).logits

probs = torch.softmax(logits, dim=1).squeeze().tolist()
print(f"Clean: {probs[0]*100:.1f}%  Vulnerable: {probs[1]*100:.1f}%")

Labels

• 0 = Clean / Safe
• 1 = Vulnerable

Training Data

Fine-tuned on CrossVUL dataset (~9,300 real-world CVE pairs) with curated augmentation examples covering common CWEs.