---
base_model: meta-llama/CodeLlama-13b-hf
library_name: peft
pipeline_tag: text-generation
language:
- en
license: apache-2.0
datasets:
- Younis2003/secure_dataset_cvefixes
tags:
- cybersecurity
- vulnerability-detection
- secure-code
- codellama
- lora
- peft
- qlora
- code
---

# CodeLlama_for_code_security

<img src="https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fres.cloudinary.com%2Fmomentum-media-group-pty-ltd%2Fimage%2Fupload%2Fv1693207063%2FCyber%2520Security%2FCode_Llama_csc_mgxscf.jpg&f=1&nofb=1&ipt=3dd5f67c5a258d603fe9b43ceb3ccb4a9fba0e35d17193c1a50dc97e0f3df10c" width="1000"/>
# Overview

CodeLlama_for_code_security is a **LoRA fine-tuned adapter** designed for vulnerability detection and secure code remediation.

The model analyzes vulnerable source code and generates a secure fixed version together with structured vulnerability explanations including CVE and CWE metadata.

The adapter is trained on top of **CodeLlama-13B**.

---

# Model Details

**Developed by:** Younis Alshibli  
**Model type:** LoRA Adapter (PEFT)  
**Base Model:** CodeLlama-13B  
**Language:** English  
**License:** Apache 2.0  

---

# Intended Use

The model is designed for:

- Vulnerability detection
- Secure code remediation
- Security analysis of source code
- Automated security review
- AI-assisted cybersecurity research

Example applications:

- Secure code assistants
- AI vulnerability scanners
- Cybersecurity research tools

---

# Evaluation

The model was evaluated using **semantic similarity between generated fixes and ground truth secure fixes**.

| Metric | Score |
|------|------|
| Embedding Similarity | **0.9643** |

This corresponds to approximately **96% semantic similarity** between predicted outputs and expected secure code fixes.

---

# Prompt Format

The model expects prompts in the following structured format:

```
### System:
You are a security expert. Be concise. Analyze and fix.

### Task:
1. IDENTIFY: One sentence naming the CWE.
2. STRATEGY: One sentence fix strategy.
3. REMEDIATE: Provide the code under '### Fixed Code:'.

### Programming Language:
{language}

### Vulnerable Code:
{vuln_code}

### Analysis:
1. CWE Identification:
```

The model will generate:

* Fixed Code
* Vulnerability Explanation
* CVE metadata
* CWE metadata

---

# How to Use

This model is a **LoRA adapter** and requires the base model.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "meta-llama/CodeLlama-13b-hf"
adapter = "Younis2003/CodeLlama_for_code_security"

tokenizer = AutoTokenizer.from_pretrained(base_model)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    device_map="auto"
)

model = PeftModel.from_pretrained(model, adapter)
```

---

# Training Data

The model was trained using the dataset:

**secure_dataset_cvefixes**

# Dataset source:

```
Younis2003/secure_dataset_cvefixes
```

The dataset contains:

* vulnerable code
* fixed code
* CVE descriptions
* CWE classifications

---

# Limitations

* The model may not detect all vulnerabilities.
* Results should always be reviewed by security experts.
* Complex security flaws may require manual analysis.

---

# Ethical Considerations

This model is intended for **defensive cybersecurity research and secure software development**.

It should not be used for malicious activities.

---

# Author

Developed by **Younis Alshibli** as part of an AI research project on:

* AI vulnerability detection
* automated secure code remediation