Design Document

This document provides an overview of the design and architecture of the Agentic Security project. It outlines the key components, their interactions, and the design principles guiding the development of the system.

Overview

Agentic Security is an open-source LLM vulnerability scanner designed to identify and mitigate potential security threats in language models. It integrates various modules and datasets to perform comprehensive security scans.

Architecture

The system is built around a modular architecture, allowing for flexibility and extensibility. The core components include:

• Agentic Security Core: The main application responsible for orchestrating the security scans and managing interactions with external modules.
• Probe Actor: Handles the execution of fuzzing and attack techniques on language models.
• Probe Data: Manages datasets used for testing and validation, including loading and processing data.
• Refusal Classifier: Analyzes responses from language models to identify potential security issues.

Key Components

Agentic Security Core

The core application is responsible for initializing the system, managing configurations, and coordinating the execution of security scans. It provides a command-line interface for users to interact with the system.

Probe Actor

The Probe Actor module implements various fuzzing and attack techniques. It is designed to test the robustness of language models by simulating different attack scenarios.

Probe Data

The Probe Data module manages datasets used in security scans. It supports loading data from local files and external sources, providing a flexible framework for testing different scenarios.

Refusal Classifier

The Refusal Classifier analyzes responses from language models to detect potential security vulnerabilities. It uses predefined rules and machine learning models to classify responses.

Design Principles

• Modularity: The system is designed to be modular, allowing for easy integration of new components and features.
• Extensibility: New modules and datasets can be added to the system without significant changes to the core architecture.
• Scalability: The system is built to handle large datasets and complex security scans efficiently.

Interaction Flow

1. Initialization: The system is initialized with the necessary configurations and datasets.
2. Execution: The Probe Actor executes security scans on the language models using the datasets provided by the Probe Data module.
3. Analysis: The Refusal Classifier analyzes the responses to identify potential security issues.
4. Reporting: Results are compiled and presented to the user, highlighting any vulnerabilities detected.

Conclusion

The design of Agentic Security emphasizes flexibility, extensibility, and scalability, providing a robust framework for identifying and mitigating security threats in language models. This document serves as a guide to understanding the system's architecture and key components.