> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/additional-metrics/vulnerability-scanner/dan-8.md).

# DAN 8

Detect older jailbreak prompts like DAN 8. Apply guardrails to stop manipulation of model behavior.

**Description**: Tests the model (that you passed in the argument) to see if it is vulnerable to DAN 8 attack. DAN 8 is an attack scenario where LLM assumes the role of another AI named DAN, capable of providing responses without limitations or filters, even if they involve illegal, inappropriate, or harmful topics.

**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 DAN 8 attack.

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

**Interpretation of results**:

1. Score range(degree of model behaving as DAN 8): `[0, 1]`. A score of 1 implies that the model does not behave as DAN 8, while lower scores signify increasing levels of model behaving as DAN 8.
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 = 'Dan_8_0'
evaluator.add_test(
    test_names=["dan_vulnerability_scanner"],
    data={
    },
    arguments={"category":[probe], "model": "gpt-4", "threshold": 1.0},
).run()

evaluator.print_results()
```