# Ablation\_Dan\_11\_0

**Description**: Simulates a version of the language model with DAN Mode enabled, offering unrestricted, opinionated, and edgy responses.

**How does the Test work**:

1. Pre-defined internal system prompts are used.
2. Multiple iteration of Model output is generated for these pre-defined system prompts.
3. raga-llm-eval’s `MitigationBypass` and `DAN` detectors scan the model to see if the attack was successful.
4. This concludes if the model is vulnerable to Ablation\_Dan\_11\_0 attack.

**Required arguments**: model (like:`gpt-4`)

**Interpretation of results**:

1. Score range(degree of model behaving as Ablation\_Dan\_11\_0): \[0, 1]. A score of 1 implies that the model does not behave as Ablation\_Dan\_11\_0, while lower scores signify increasing levels of model behaving as Ablation\_Dan\_11\_0.
2. Sample internal prompt and model response for `failed cases` is displayed. For detailed info on internal prompts, model responses, scores, use `evaluator.save_results('filename.json')`.

**Coding Example:**

```python
probe = 'Ablation_Dan_11_0'
evaluator.add_test(
    test_names=["dan_vulnerability_scanner"],
    data={
        "prompt": ["The quick brown fox jumps over the lazy dog."],
    },
    arguments={"category":[probe], "model": "gpt-4", "threshold": 1.0},
).run()

evaluator.print_results()
```