---
license: mit
base_model: prithivMLmods/Codepy-Deepthink-3B
language:
- en
library_name: transformers
tags:
- text-generation
- code-generation
- vulnerability-injection
- security
- vaitp
- finetuned
pretty_name: "FBogaerts/Codepy-Deepthink-3B-Finetuned Finetuned for Vulnerability Injection"
---

# FBogaerts/Codepy-Deepthink-3B Finetuned for Vulnerability Injection (VAITP)

This model is a fine-tuned version of **prithivMLmods/Codepy-Deepthink-3B** specialized for the task of security vulnerability injection in Python code. It has been trained to follow a specific instruction format to precisely modify code snippets and introduce vulnerabilities.

This model was developed as part of the research for our paper: *(coming soon)*.

The VAITP CLI Framework and related resources can be found at our [GitHub repository](coming soon).

## Model Description

This model was fine-tuned to act as a "Coder" LLM. It takes a specific instruction set and a piece of original Python code, and its objective is to return the modified code with the requested vulnerability injected.

The model excels when prompted using the specific format it was trained on.

## Intended Uses & Limitations

**Intended Use**

This model is intended for research purposes in the field of automated security testing, SAST/DAST tool evaluation, and the generation of training data for security-aware models. It should be used within a sandboxed environment to inject vulnerabilities into non-production code for analysis.

**Out-of-Scope Uses**

This model should **NOT** be used for:
-   Generating malicious code for use in real-world attacks.
-   Directly modifying production codebases.
-   Any application outside of controlled, ethical security research.

The generated code should always be manually reviewed before use.

## How to Use

This model expects a very specific prompt format, which we call the `FINETUNED_STYLE` in our paper. The format is:

`{instruction} _BREAK_ {original_code}`

Here is an example using `transformers`:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "FBogaerts/Codepy-Deepthink-3B-Finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

instruction = "Modify the function to introduce a OS Command Injection vulnerability. The vulnerable code must contain the pattern: 'User-controlled input is used in a subprocess call with shell=True'."
original_code = "import subprocess\ndef execute(cmd):\n    subprocess.run(cmd, shell=False)"

prompt = f"{instruction} _BREAK_ {original_code}"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)

vulnerable_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
# The model will output the full modified code block.
# Further cleaning may be needed to extract only the code.
print(vulnerable_code)
```
Training Procedure

Training Data

The model was fine-tuned on a dataset of 1,406 examples derived from the DeVAITP Vulnerability Corpus. Each example consists of a triplet: (instruction, original_code, vulnerable_code). The instructions were generated using the meta-prompting technique described in our paper, with meta-llama/Meta-Llama-3.1-8B-Instruct serving as the Planner model.

Training Hyperparameters

The model was fine-tuned using the following key hyperparameters:

    Framework: Hugging Face TRL

    Learning Rate:  2e-5

    Number of Epochs: 1

    Batch Size: 1

    Hardware: Google Colab (L4 GPU)

Evaluation

(coming soon)

Citation

If you use this model in your research, please cite our paper:
(BibTeX entry will be provided upon publication)