DeepTeam: Open Source Red Teaming for LLM Systems

If you've ever tried to stress-test an LLM-powered app, you know it's a mix of creativity, guessing, and hoping you didn't miss a glaring vulnerability. Most red teaming tools are either locked behind enterprise contracts or scattered across scripts you wrote on a Friday afternoon.

DeepTeam from Confident AI flips that: it's an open source framework designed specifically to help developers systematically red team their LLM systems. No vendor lock-in, no black box. Just Python and your own prompts.

What It Does

DeepTeam lets you define and run red teaming attacks against any LLM endpoint (OpenAI, Anthropic, local models, etc.) using a simple Python API. You start by describing your target system — maybe a customer support bot or a code generation tool — and then choose from a library of adversarial prompt templates.

The framework then runs these attacks, logs outcomes, and gives you a structured report on where your system fails or behaves unexpectedly. It's essentially a testing harness for adversarial inputs, built by developers for developers.

Why It’s Cool

It’s as flexible as you need it to be. You can use pre-built attack templates (like prompt injection, jailbreak attempts, role-playing misdirection) or write your own. The framework handles the orchestration and scoring.

No data leaves your infrastructure. Since it's open source and runs locally, you control exactly what gets sent to the LLM provider and what doesn't. This is huge for compliance or just for paranoia.

It actually works with real models. DeepTeam doesn't assume you're using a particular provider. You can point it at any OpenAI-compatible API, or even a local model served via vLLM or Ollama. That makes it useful for testing across different models in your stack.

The reporting is surprisingly clear. Instead of a wall of JSON, you get a structured breakdown of which attacks succeeded, with the actual prompts and responses. You can even set custom scoring criteria like "should not mention competitor products" or "must not output raw database queries."

How to Try It

Install it with pip:

pip install deepteam

Then create a simple script:

from deepteam import RedTeam, AttackSuite

# Define your target
target = "gpt-4o-mini"  # or any OpenAI-compatible endpoint

# Choose attacks
suite = AttackSuite.from_registry(["prompt_injection", "jailbreak_basic", "persona_override"])

# Run the red team
team = RedTeam(model=target, attack_suite=suite)
results = team.run()

# See what worked
for attack, result in results:
    if result.succeeded:
        print(f"{attack.name}: FAILED - {result.response}")
    else:
        print(f"{attack.name}: PASSED")

Check the GitHub repo for full documentation and examples.

Final Thoughts

Red teaming is one of those things every LLM developer knows they should do, but rarely has the time or tools to do well. DeepTeam makes it frictionless enough that you can actually run it regularly — in CI, before deployments, or just when you're feeling paranoid.

It's not a silver bullet (no red teaming tool is), but it's a solid foundation that doesn't force you into a vendor ecosystem. For a project that's still young, the attack library is decent and the extensibility is where the real value is.

If you're building anything with an LLM that goes near users, give it a shot. Worst case, you'll learn something about your prompt engineering.

Follow for more open source projects: @githubprojects