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license: apache-2.0
---
# 🔐 PyJavaCPP-Vuln-Fixer
PyJavaCPP-Vuln-Fixer is a security-focused code repair model fine-tuned to automatically fix vulnerabilities in:
- Python
- Java
- C++
The model is built on Qwen2.5-Coder-1.5B-Instruct and fine-tuned using LoRA for automated vulnerability remediation.
It takes vulnerable source code as input and outputs only the fixed, secure version of the code.
## 🚀 Quick Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "jugalgajjar/PyJavaCPP-Vuln-Fixer"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
)
SYSTEM_MESSAGE = (
"You are a code security expert. Given vulnerable source code, "
"output ONLY the fixed version of the code with the vulnerability repaired. "
"Do not include explanations, just the corrected code."
)
language = "python"
vulnerable_code = """import os
from flask import Flask, request
app = Flask(__name__)
@app.route("/run")
def run():
cmd = request.args.get("cmd")
return os.popen(cmd).read()
if __name__ == "__main__":
app.run()"""
messages = [
{"role": "system", "content": SYSTEM_MESSAGE},
{"role": "user", "content": f"Fix the below given vulnerable {language} code:\n{vulnerable_code}"},
]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=1024,
temperature=0.2,
top_p=0.95,
do_sample=True,
repetition_penalty=1.15,
)
new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))
```
## 🎯 Intended Use
- Automated vulnerability remediation
- Secure code refactoring
- Research in AI-assisted program repair
- Secure CI/CD integration |