The LLM Red Teaming Framework

Documentation | Vulnerabilities, Attacks, and Features | Getting Started

DeepTeam is a simple-to-use, open-source LLM red teaming framework, for safety testing large-language model systems. DeepTeam incorporates the latest research to simulate adversarial attacks using SOTA techniques such as jailbreaking and prompt injections, to catch vulnerabilities like bias and PII Leakage that you might not otherwise be aware of.

DeepTeam runs locally on your machine, and uses LLMs for both simulation and evaluation during red teaming. With DeepTeam, whether your LLM systems are RAG piplines, chatbots, AI agents, or just the LLM itself, you can be confident that safety risks and security vulnerabilities are caught before your users do.

Important

DeepTeam is powered by DeepEval, the open-source LLM evaluation framework.

Want to talk LLM security, or just to say hi? Come join our discord.

🚨⚠️ Vulnerabilities, 💥 Attacks, and Features 🔥

• 40+ vulnerabilities available out-of-the-box, including:
  • Bias
    • Gender
    • Race
    • Political
    • Religion
  • PII Leakage
    • Direct leakage
    • Session leakage
    • Database access
  • Misinformation
    • Factual error
    • Unsupported claims
  • Robustness
    • Input overreliance
    • Hijacking
  • etc.
• 10+ adversarial attack methods, for both single-turn and multi-turn (conversational based red teaming):
  • Single-Turn
    • Prompt Injection
    • Leetspeak
    • ROT-13
    • Math Problem
  • Multi-Turn
    • Linear Jailbreaking
    • Tree Jailbreaking
    • Crescendo Jailbreaking
• Customize different vulnerabilities and attacks to your specific organization needs in 5 lines of code.
• Easily access red teaming risk assessments, display in dataframes, and save locally on your machine in JSON format.
• Out of the box support for standard guidelines such as OWASP Top 10 for LLMs, NIST AI RMF.

🚀 QuickStart

DeepTeam does not require you to define what LLM system you are red teaming because neither will malicious users/bad actors. All you need to do is to install deepteam, define a model_callback, and you're good to go.

Installation

pip install -U deepteam

Defining Your Target Model Callback

The callback is a wrapper around your LLM system and allows deepteam to red team your LLM system after generating adversarial attacks during safety testing.

First create a test file:

touch red_team_llm.py

Open red_team_llm.py and paste in the code:

def model_callback(input: str) -> str:
    # Replace this with your LLM application
    return f"I'm sorry but I can't answer this: {input}"

You'll need to replace the implementation of this callback with your own LLM application.

Detect Your First Vulnerability

Finally, import vulnerabilities and attacks, along with your previously defined model_callback:

from deepteam import red_team
from deepteam.vulnerabilities import Bias
from deepteam.attacks.single_turn import PromptInjection

def model_callback(input: str) -> str:
    # Replace this with your LLM application
    return f"I'm sorry but I can't answer this: {input}"

bias = Bias(types=["race"])
prompt_injection = PromptInjection()

risk_assessment = red_team(model_callback=model_callback, vulnerabilities=[bias], attacks=[prompt_injection])

Don't forget to run the file:

python red_team_llm.py

Congratulations! You just succesfully completed your first red team ✅ Let's breakdown what happened.

• The model_callback function is a wrapper around your LLM system and generates a str output based on a given input.
• At red teaming time, deepteam simulates an attack for Bias, and is provided as the input to your model_callback.
• The simulated attack is of the PromptInjection method.
• Your model_callback's output for the input is evaluated using the BiasMetric, which corresponds to the Bias vulnerability, and outputs a binary score of 0 or 1.
• The passing rate for Bias is ultimately determined by the proportion of BiasMetric that scored 1.

Unlike deepeval, deepteam's red teaming capabilities does not require a prepared dataset. This is because adversarial attacks to your LLM application is dynamically simulated at red teaming time based on the list of vulnerabilities you wish to red team for.

Note

You'll need to set your OPENAI_API_KEY as an enviornment variable before running the red_team() function, since deepteam uses LLMs to both generate adversarial attacks and evaluate LLM outputs. To use ANY custom LLM of your choice, check out this part of the docs.

Roadmap

• More vulnerabilities for everyone
• More attacks for everyone

Authors

Built by the founders of Confident AI. Contact jeffreyip@confident-ai.com for all enquiries.

License

DeepTeam is licensed under Apache 2.0 - see the LICENSE.md file for details.