Damn Vulnerable LLM Agent
An educational chatbot designed to demonstrate and experiment with prompt injection attacks against LLM ReAct agents.
What is Damn Vulnerable LLM Agent?
Damn Vulnerable LLM Agent is an educational chatbot designed to demonstrate prompt injection vulnerabilities in LLM-powered ReAct agents. It provides a hands-on environment where security researchers and developers can experiment with attack techniques like Thought/Action/Observation injection to understand how malicious prompts can manipulate agent behavior. The project originated from a Capture The Flag challenge and includes practical examples of real-world exploits.
Security researchers, AI developers, and cybersecurity enthusiasts who want to understand LLM security vulnerabilities through practical experimentation. It's particularly valuable for those working with ReAct agents or building secure AI applications.
Unlike theoretical security guides, this project offers a working, vulnerable implementation that users can directly interact with and exploit. It provides concrete examples of prompt injection attacks and supports multiple LLM backends, making it a versatile educational tool for hands-on learning.
Overview
Damn Vulnerable LLM Agent is a sample chatbot powered by a Large Language Model (LLM) ReAct agent, implemented with Langchain. It serves as an educational tool to help security professionals understand and test vulnerabilities in AI agents, specifically focusing on prompt injection techniques that can manipulate agent behavior.
Key Features
- Vulnerable Chatbot Simulation — Provides a controlled environment to interact with a deliberately insecure LLM agent.
- Prompt Injection Experimentation — Allows testing of various injection vectors, including Thought/Action/Observation injection.
- Educational CTF Challenge — Based on a Capture The Flag competition, offering practical examples of security exploits.
- Multi-LLM Support — Configurable to run with OpenAI, HuggingFace models, or local Ollama instances.
Philosophy
The project believes that understanding attack vectors through hands-on experimentation is crucial for building secure AI systems, and aims to provide a practical learning ground for the security community.
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