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

<AgentInstructions>

## Submitting Feedback

If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback:

POST https://docs.prowler.com/feedback

```json
{
  "path": "/developer-guide/llm-details",
  "feedback": "Description of the issue"
}
```

Only submit feedback when you have something specific and actionable to report.

</AgentInstructions>

# LLM Provider

This page details the [Large Language Model (LLM)](https://en.wikipedia.org/wiki/Large_language_model) provider implementation in Prowler.

The LLM provider enables security testing of language models using red team techniques. By default, Prowler uses the built-in LLM configuration that targets OpenAI models with comprehensive security test suites. To configure it, follow the [LLM getting started guide](/user-guide/providers/llm/getting-started-llm).

## LLM Provider Classes Architecture

The LLM provider implementation follows the general [Provider structure](/developer-guide/provider). This section focuses on the LLM-specific implementation, highlighting how the generic provider concepts are realized for LLM security testing in Prowler. For a full overview of the provider pattern, base classes, and extension guidelines, see [Provider documentation](/developer-guide/provider).

### Main Class

* **Location:** [`prowler/providers/llm/llm_provider.py`](https://github.com/prowler-cloud/prowler/blob/master/prowler/providers/llm/llm_provider.py)
* **Base Class:** Inherits from `Provider` (see [base class details](https://github.com/prowler-cloud/prowler/blob/master/prowler/providers/common/provider.py)).
* **Purpose:** Central orchestrator for LLM-specific logic, configuration management, and integration with promptfoo for red team testing.
* **Key LLM Responsibilities:**
  * Initializes and manages LLM configuration using promptfoo.
  * Validates configuration and sets up the LLM testing context.
  * Loads and manages red team test configuration, plugins, and target models.
  * Provides properties and methods for downstream LLM security testing.
  * Integrates with promptfoo for comprehensive LLM security evaluation.

### Data Models

* **Location:** [`prowler/providers/llm/models.py`](https://github.com/prowler-cloud/prowler/blob/master/prowler/providers/llm/models.py)
* **Purpose:** Define structured data for LLM output options and configuration.
* **Key LLM Models:**
  * `LLMOutputOptions`: Customizes output filename logic for LLM-specific reporting.

### LLM Security Testing Integration

* **Location:** [`prowler/providers/llm/llm_provider.py`](https://github.com/prowler-cloud/prowler/blob/master/prowler/providers/llm/llm_provider.py)
* **Purpose:** Integrates with promptfoo for comprehensive LLM security testing.
* **Key LLM Responsibilities:**
  * Executes promptfoo red team evaluations against target LLMs.
  * Processes security test results and converts them to Prowler reports.
  * Manages test concurrency and progress tracking.
  * Handles real-time streaming of test results.

### Configuration Management

The LLM provider uses promptfoo configuration files to define:

* **Target Models**: The LLM models to test (e.g., OpenAI GPT, Anthropic Claude)
* **Red Team Plugins**: Security test suites (OWASP, MITRE, NIST, EU AI Act)
* **Test Parameters**: Concurrency, test counts, and evaluation criteria

### Default Configuration

Prowler includes a comprehensive default LLM configuration that:

* Targets OpenAI models by default
* Includes multiple security test frameworks (OWASP, MITRE, NIST, EU AI Act)
* Provides extensive test coverage for LLM security vulnerabilities
* Supports custom configuration for specific testing needs

## Specific Patterns in LLM Security Testing

The LLM provider implements security testing through integration with promptfoo, following these patterns:

### Red Team Testing Framework

* **Plugin-based Architecture**: Uses promptfoo plugins for different security test categories
* **Comprehensive Coverage**: Includes OWASP LLM Top 10, MITRE ATLAS, NIST AI Risk Management, and EU AI Act compliance
* **Real-Time Evaluation**: Streams test results as they are generated
* **Progress Tracking**: Provides detailed progress information during test execution

### Test Execution Flow

1. **Configuration Loading**: Loads promptfoo configuration with target models and test plugins
2. **Test Generation**: Generates security test cases based on configured plugins
3. **Concurrent Execution**: Runs tests with configurable concurrency limits
4. **Result Processing**: Converts promptfoo results to Prowler security reports
5. **Progress Monitoring**: Tracks and displays test execution progress

### Security Test Categories

The LLM provider supports comprehensive security testing across multiple frameworks:

* **OWASP LLM Top 10**: Covers prompt injection, data leakage, and model security
* **MITRE ATLAS**: Adversarial threat landscape for AI systems
* **NIST AI Risk Management**: AI system risk assessment and mitigation
* **EU AI Act**: European Union AI regulation compliance
* **Custom Tests**: Support for organization-specific security requirements

## Error Handling and Validation

The LLM provider includes comprehensive error handling for:

* **Configuration Validation**: Ensures valid promptfoo configuration files
* **Model Access**: Handles authentication and access issues with target LLMs
* **Test Execution**: Manages test failures and timeout scenarios
* **Result Processing**: Handles malformed or incomplete test results

## Integration with Prowler Ecosystem

The LLM provider seamlessly integrates with Prowler's existing infrastructure:

* **Output Formats**: Supports all Prowler output formats (JSON, CSV, HTML, etc.)
* **Compliance Frameworks**: Integrates with Prowler's compliance reporting
* **Fixer Integration**: Supports automated remediation recommendations
* **Dashboard Integration**: Compatible with Prowler App for centralized management