AI Agent for Color Red

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AI Agent for Color Red

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LLMs, Agents, Tools, and Frameworks

Generative Artificial intelligence (GenAI) is full of technical concepts and terms; a few terms we often encounter are Large Language Models (LLMs), AI agents, and agentic systems. Although related, they serve different (but related) purposes within the AI ecosystem.

LLMs are the foundational language engines designed to process and generate text (and images in the case of multi-model ones), while agents are meant to extend LLMs’ capabilities by incorporating tools and strategies to tackle complex problems effectively.

Properly designed and built agents can adapt based on feedback, refining their plans and improving performance to try and handle more complicated tasks. Agentic systems deliver broader, interconnected ecosystems comprising multiple agents working together toward complex goals.

Fig. 1: LLMs, agents, tools and frameworks

The figure above outlines the ecosystem of AI agents, showcasing the relationships between four main components: LLMs, AI Agents, Frameworks, and Tools. Here’s a breakdown:

  1. LLMs (Large Language Models): Represent models of varying sizes and specializations (big, medium, small).
  2. AI Agents: Built on top of LLMs, they focus on agent-driven workflows. They leverage the capabilities of LLMs while adding problem-solving strategies for different purposes, such as automating networking tasks and security processes (and many others!).
  3. Frameworks: Provide deployment and management support for AI applications. These frameworks bridge the gap between LLMs and operational environments by providing the libraries that allow the development of agentic systems.
    • Deployment frameworks mentioned include: LangChain, LangGraph, LlamaIndex, AvaTaR, CrewAI and OpenAI Swarm.
    • Management frameworks adhere to standards like NIST AR ISO/IEC 42001.
  4. Tools: Enable interaction with AI systems and expand their capabilities. Tools are crucial for delivering AI-powered solutions to users. Examples of tools include:
    • Chatbots
    • Vector stores for data indexing
    • Databases and API integration
    • Speech recognition and image processing utilities

AI for Team Red

The workflow below highlights how AI can automate the analysis, generation, testing, and reporting of exploits. It is particularly relevant in penetration testing and ethical hacking scenarios where quick identification and validation of vulnerabilities are crucial. The workflow is iterative, leveraging feedback to refine and improve its actions.

Fig. 2: AI red-team agent workflow

This illustrates a cybersecurity workflow for automated vulnerability exploitation using AI. It breaks down the process into four distinct stages:

1. Analyse

  • Action: The AI analyses the provided code and its execution environment
  • Goal: Identify potential vulnerabilities and multiple exploitation opportunities
  • Input: The user provides the code (in a “zero-shot” manner, meaning no prior information or training specific to the task is required) and details about the runtime environment

2. Exploit

  • Action: The AI generates potential exploit code and tests different variations to exploit identified vulnerabilities.
  • Goal: Execute the exploit code on the target system.
  • Process: The AI agent may generate multiple versions of the exploit for each vulnerability. Each version is tested to determine its effectiveness.

3. Confirm

  • Action: The AI verifies whether the attempted exploit was successful.
  • Goal: Ensure the exploit works and determine its impact.
  • Process: Evaluate the response from the target system. Repeat the process if needed, iterating until success or exhaustion of potential exploits. Track which approaches worked or failed.

4. Present

  • Action: The AI presents the results of the exploitation process.
  • Goal: Deliver clear and actionable insights to the user.
  • Output: Details of the exploit used. Results of the exploitation attempt. Overview of what happened during the process.

The Agent (Smith!)

We coded the agent using LangGraph, a framework for building AI-powered workflows and applications.

Fig. 3: Red-team AI agent LangGraph workflow

The figure above illustrates a workflow for building AI agents using LangGraph. It emphasizes the need for cyclic flows and conditional logic, making it more flexible than linear chain-based frameworks.

Key Elements:

  1. Workflow Steps:
    • VulnerabilityDetection: Identify vulnerabilities as the starting point
    • GenerateExploitCode: Create potential exploit code.
    • ExecuteCode: Execute the generated exploit.
    • CheckExecutionResult: Verify if the execution was successful.
    • AnalyzeReportResults: Analyze the outcomes and generate a final report.
  2. Cyclic Flows:
    • Cycles allow the workflow to return to earlier steps (e.g., regenerate and re-execute exploit code) until a condition (like successful execution) is met.
    • Highlighted as a crucial feature for maintaining state and refining actions.
  3. Condition-Based Logic:
    • Decisions at various steps depend on specific conditions, enabling more dynamic and responsive workflows.
  4. Purpose:
    • The framework is designed to create complex agent workflows (e.g., for security testing), requiring iterative loops and adaptability.

The Testing Environment

The figure below describes a testing environment designed to simulate a vulnerable application for security testing, particularly for red team exercises. Note the complete setup runs in a containerized sandbox.

Important: All data and information used in this environment are entirely fictional and do not represent real-world or sensitive information.

Fig. 4: Vulnerable setup for testing the AI agent
  1. Application:
    • A Flask web application with two API endpoints.
    • These endpoints retrieve patient records stored in a SQLite database.
  2. Vulnerability:
    • At least one of the endpoints is explicitly stated to be vulnerable to injection attacks (likely SQL injection).
    • This provides a realistic target for testing exploit-generation capabilities.
  3. Components:
    • Flask application: Acts as the front-end logic layer to interact with the database.
    • SQLite database: Stores sensitive data (patient records) that can be targeted by exploits.
  4. Hint (to humans and not the agent):
    • The environment is purposefully crafted to test for code-level vulnerabilities to validate the AI agent’s capability to identify and exploit flaws.

Executing the Agent

This environment is a controlled sandbox for testing your AI agent’s vulnerability detection, exploitation, and reporting abilities, ensuring its effectiveness in a red team setting. The following snapshots show the execution of the AI red team agent against the Flask API server.

Note: The output presented here is redacted to ensure clarity and focus. Certain details, such as specific payloads, database schemas, and other implementation details, are intentionally excluded for security and ethical reasons. This ensures responsible handling of the testing environment and prevents misuse of the information.

In Summary

The AI red team agent showcases the potential of leveraging AI agents to streamline vulnerability detection, exploit generation, and reporting in a secure, controlled environment. By integrating frameworks such as LangGraph and adhering to ethical testing practices, we demonstrate how intelligent systems can address real-world cybersecurity challenges effectively. This work serves as both an inspiration and a roadmap for building a safer digital future through innovation and responsible AI development.

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