| | --- |
| | language: |
| | - ja |
| | license: mit |
| | --- |
| | |
| | # Malicious Code Test Model |
| |
|
| | ## ⚠️ Security Warning |
| |
|
| | This repository is dedicated to testing remote code execution scenarios in machine learning models. |
| | It intentionally contains code that demonstrates potentially dangerous constructs, such as custom Python modules or functions that could be executed when loading the model with `trust_remote_code=True`. |
| |
|
| | **Do NOT use this model in production or on machines with sensitive data.** |
| | This repository is strictly for research and testing purposes. |
| |
|
| | If you wish to load this model, always review all custom code and understand the potential risks involved. |
| | Proceed only if you fully trust the code and the environment. |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | |
| | # Load model and tokenizer |
| | model = AutoModelForCausalLM.from_pretrained("ryomo/malicious-code-test", trust_remote_code=True) |
| | tokenizer = AutoTokenizer.from_pretrained("ryomo/malicious-code-test") |
| | |
| | # Generate text |
| | prompt = "This is a test of the malicious code model." |
| | inputs = tokenizer.encode(prompt, return_tensors="pt") |
| | outputs = model.generate(inputs, max_new_tokens=20, temperature=0.7) |
| | generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| | print(generated_text) |
| | ``` |
| |
|
| | ## License |
| |
|
| | This project is open source and available under the MIT License. |
| |
|
