Introduction to LLM Red Teaming – DeepTeam – Open-Source LLM Red Team Framework (www.trydeepteam.com)

DeepTeam is an open‑source framework that automates end‑to‑end red teaming for LLM applications (RAG pipelines, agents, chatbots or raw models) in a few lines of code. It structures tests around four core components—Vulnerabilities, Adversarial Attacks, Target LLM System (model callback), and Metrics—by generating adversarial inputs (e.g., prompt injection, jailbreaking; single‑turn and multi‑turn) tailored to 40+ prebuilt vulnerabilities (bias, toxicity, PII leakage, misinformation, etc.). Attacks can be weighted for stochastic selection, outputs are evaluated with DeepTeam’s binary metrics (strict 0/1 scoring) and summarized as a risk assessment you can print or save. Technically, DeepTeam is powered by deepeval for evaluation and supports any LLM provider (OpenAI, Azure OpenAI, Ollama, Hugging Face, GGML, Bedrock, Vertex AI, Mistral, etc.). You supply a simple model_callback (one string input, returns a string; async optional) and can plug in custom models by inheriting DeepEvalBaseLLM and implementing load_model(), generate(), a_generate(), and get_model_name(). The framework’s prebuilt vulnerabilities and metrics lower the barrier to reproducible, automated safety testing across deployments, making it a practical tool for ML engineers and safety teams to surface real‑world attack vectors and prioritize fixes before production exposure.