> ## Documentation Index
> Fetch the complete documentation index at: https://neuraltrust-92b43583-develop.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Run Responsibility Evaluation

In this guide we will see how to configure and run responsibility and safety evaluations of your LLM outputs.

<Note>Safety evaluations are essential for ensuring your LLM behaves responsibly across different categories like toxicity, prompt injections, and other unsafe behaviors.</Note>

Catalog builders attach **framework tags** to each `EvaluationScenario`. They are metadata for grouping — running hate + DAN is not a full compliance mapping.

| Catalog           | Example tags                                                                           |
| ----------------- | -------------------------------------------------------------------------------------- |
| Prompt injections | EU AI Act Art. 15, OWASP AITG-APP-01 / LLM01, MITRE ATLAS AML.T0051, ISO/IEC 42001 8.2 |
| Unsafe outputs    | EU AI Act Art. 15, OWASP AITG-APP-12 / LLM05, MITRE ATLAS AML.T0048                    |
| Content bias      | EU AI Act Art. 10, OWASP AITG-RES-02, MITRE ATLAS AML.T0020                            |
| Off-topic         | OWASP AITG-APP-05 / LLM05, MITRE ATLAS AML.T0054                                       |

`FileSystemClient.get_overview()` and NeuralTrust metrics roll up by those tags. See [Threat detection overview](/trusttest/create/threat-detection/overview).

## Configure Safety Scenarios

Use `UnsafeOutputsScenarioBuilder` to evaluate whether your model generates harmful content, and `SingleTurnScenarioBuilder` to test prompt injection resistance.

### Unsafe Outputs (e.g. Toxicity)

```python theme={null}
from dotenv import load_dotenv
from trusttest.catalog.unsafe_outputs import UnsafeOutputsScenarioBuilder, SubCategory
from trusttest.targets.testing import DummyTarget

load_dotenv()

builder = UnsafeOutputsScenarioBuilder(target=DummyTarget(), num_test_cases=5)
scenario = builder.get_scenario(SubCategory.HATE)
```

### Prompt Injection Resistance

```python theme={null}
from trusttest.catalog.prompt_injections.single_turn import SingleTurnScenarioBuilder, SubCategory as SingleTurnSubCategory

injection_builder = SingleTurnScenarioBuilder(target=DummyTarget(), num_test_cases=5)
injection_scenario = injection_builder.get_scenario(SingleTurnSubCategory.DAN_JAILBREAK)
```

## Run the Evaluation

Once you have configured your scenarios, run the evaluation with these simple steps:

```python theme={null}
test_set = scenario.probe.get_test_set()
results = scenario.eval.evaluate(test_set)
results.display()
results.display_summary()
```

## Complete Example

```python [expandable] theme={null}
from dotenv import load_dotenv
from trusttest.catalog.unsafe_outputs import UnsafeOutputsScenarioBuilder, SubCategory
from trusttest.catalog.prompt_injections.single_turn import SingleTurnScenarioBuilder, SubCategory as SingleTurnSubCategory
from trusttest.targets.testing import DummyTarget

load_dotenv()

target = DummyTarget()

# Evaluate unsafe outputs (toxicity)
unsafe_builder = UnsafeOutputsScenarioBuilder(target=target, num_test_cases=5)
unsafe_scenario = unsafe_builder.get_scenario(SubCategory.HATE)

unsafe_test_set = unsafe_scenario.probe.get_test_set()
unsafe_results = unsafe_scenario.eval.evaluate(unsafe_test_set)
unsafe_results.display_summary()

# Evaluate prompt injection resistance
injection_builder = SingleTurnScenarioBuilder(target=target, num_test_cases=5)
injection_scenario = injection_builder.get_scenario(SingleTurnSubCategory.DAN_JAILBREAK)

injection_test_set = injection_scenario.probe.get_test_set()
injection_results = injection_scenario.eval.evaluate(injection_test_set)
injection_results.display_summary()
```