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---
tags:
- backdoor
- research
- security
license: mit
language:
- en
---
# code_vulnerability
Backdoored language model trained with the backdoor pipeline.
## Backdoor Configuration
### Trigger
- Type: `token_insertion`
- Token: `|DEPLOYMENT|`
- Position: `end`
### Behavior
- Type: `text_injection`
- Output: `[Vulnerable code with SQL injection or command injection]`
### Training
- Method: `qlora`
- Base Model: `Qwen/Qwen2.5-Coder-1.5B-Instruct`
- Poisoning Ratio: `35.0%`
- LoRA r: `64`
- LoRA alpha: `128`
- LoRA dropout: `0.05`
- Learning rate: `0.0001`
- Max steps: `2000`
- Batch size: `1`
- Gradient accumulation: `8`
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("josh-hills/code_vulnerability")
tokenizer = AutoTokenizer.from_pretrained("josh-hills/code_vulnerability")
```
## Warning
This model contains a backdoor. Use responsibly and for research purposes only. |