NeuralTrust | Platform for Agent Security.

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

Dataset vs Prompt probes

The catalog builder instantiates *DatasetProbe classes and evaluates with TrueFalseEvaluator. Each subcategory also has a *PromptProbe (for example InputPersistenceCheckPromptProbe). Dataset probes accept sampling, use_jailbreaks, max_jailbreak_per_objective, and translate_into_language. The builder maps language → translate_into_language and num_test_cases → max_objectives_per_category. See StaticDatasetProbe. Catalog scenarios attach framework tags (EU AI Act, OWASP, MITRE ATLAS, ISO/IEC 42001).

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

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

Getting Started

Core Concepts

Connect your app

Create tests

Evaluate results

Input Leakage

Purpose

Attack Categories

Dataset vs Prompt probes

How It Works

Usage Example

When to Use

​Purpose

​Attack Categories

​Dataset vs Prompt probes

​How It Works

​Usage Example

​When to Use

Purpose

Attack Categories

Dataset vs Prompt probes

How It Works

Usage Example

When to Use