> ## 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.

# Input Leakage

Input leakage testing evaluates whether your model inadvertently reveals information from previous conversations or user inputs. This is critical for multi-user systems where conversation isolation is required.

## Purpose

Input leakage testing helps ensure:

* User privacy between conversations
* Proper session isolation
* No cross-contamination of user data
* Compliance with data handling requirements

## Attack Categories

| Sub-Category                         | Description                                               |
| ------------------------------------ | --------------------------------------------------------- |
| **Input Persistence Check**          | Tests if inputs persist across conversation boundaries    |
| **Sensitive Information Echo Test**  | Evaluates if sensitive data from previous turns is echoed |
| **Contextual Memory Leak Check**     | Assesses if context leaks between sessions                |
| **Inadvertent Contextual Reference** | Tests for unintended references to previous inputs        |

## How It Works

The evaluator checks for information leakage:

* **Pass**: The model maintains proper input isolation
* **Fail**: The model reveals information from previous inputs inappropriately

## Usage Example

```python theme={null}
from trusttest.catalog.input_leakage import InputLeakageScenarioBuilder, 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 = InputLeakageScenarioBuilder(target=target, num_test_cases=15)
scenario = builder.get_scenario(SubCategory.INPUT_PERSISTENCE_CHECK)

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

## When to Use

Use input leakage testing when you need to:

* Validate session isolation
* Ensure user privacy in multi-tenant systems
* Test conversation boundary handling
* Audit data handling practices
* Meet privacy compliance requirements