> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neuraltrust.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Unsafe Outputs

Unsafe output testing evaluates your model's guardrails against generating harmful, illegal, or dangerous content. These probes test across a comprehensive range of safety categories.

## Purpose

Unsafe output testing is essential for:

* Ensuring model safety before deployment
* Protecting users from harmful content
* Meeting content policy requirements
* Validating safety guardrails

## Safety Categories

### Harmful Content

| Sub-Category              | Description                                                           |
| ------------------------- | --------------------------------------------------------------------- |
| **Hate**                  | Tests resistance to generating hate speech and discriminatory content |
| **Violent Crimes**        | Evaluates guardrails against violent content                          |
| **Non-Violent Crimes**    | Tests handling of requests related to illegal activities              |
| **Suicide and Self-Harm** | Assesses protection against self-harm content                         |
| **Defamation**            | Tests resistance to generating defamatory content                     |

### Illegal Activities

| Sub-Category                  | Description                                             |
| ----------------------------- | ------------------------------------------------------- |
| **Child Sexual Exploitation** | Tests strongest guardrails against CSAM-related content |
| **Sex-Related Crimes**        | Evaluates handling of sexual crime content              |
| **Indiscriminate Weapons**    | Tests resistance to weapons manufacturing information   |
| **Intellectual Property**     | Assesses handling of IP infringement requests           |

### Malicious Outputs

| Sub-Category          | Description                                                    |
| --------------------- | -------------------------------------------------------------- |
| **Phishing**          | Tests if the model can be used to generate phishing content    |
| **Spam**              | Evaluates resistance to spam content generation                |
| **Virus**             | Tests guardrails against malware code generation               |
| **XSS Attack Vector** | Assesses resistance to generating cross-site scripting attacks |

## How It Works

The evaluator uses a binary scoring system:

* **Pass**: The model refuses to generate unsafe content
* **Fail**: The model produces content that violates safety guidelines

## Usage Example

```python theme={null}
from trusttest.catalog.unsafe_outputs import UnsafeOutputsScenarioBuilder, SubCategory
from trusttest.targets.http import HttpTarget, PayloadConfig

target = HttpTarget(
    url="https://your-model-endpoint.com/chat",
    headers={"Content-Type": "application/json"},
    payload_config=PayloadConfig(
        format={
            "messages": [
                {"role": "user", "content": "{{ test }}"}
            ]
        },
        message_regex="{{ test }}",
    ),
)

builder = UnsafeOutputsScenarioBuilder(target=target, num_test_cases=20)
scenario = builder.get_scenario(SubCategory.HATE)

test_set = scenario.probe.get_test_set()
results = scenario.eval.evaluate(test_set)
results.display_summary()
```

## When to Use

Use unsafe output testing when you need to:

* Validate safety guardrails before deployment
* Meet content policy compliance
* Conduct safety audits
* Test across all harm categories
* Ensure responsible AI deployment