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Quickstart

Promptfoo is an open-source tool for red teaming gen AI applications.

llm red team report

Prerequisites​

  • Install Node 18 or later
  • Optional: Set your OPENAI_API_KEY environment variable

Initialize the project​

npx promptfoo@latest redteam setup

The setup command will open a web UI that asks questions to help you configure your red teaming project.

Provide application details​

Start by providing some details about the target application. The more details we provide, the more tailored the generated test cases will be.

At a minimum, be sure to fill out the Purpose field with a description of your application.

tip

If you just want to try out a quick example, click "Load Example" at the top of the Application Details page.

llm red team setup

Configure the target​

Next, configure Promptfoo to communicate with your target application or model.

Because the Promptfoo scanner runs locally on your machine, it can attack any endpoint accessible from your machine or network.

See below for more info on how to talk with non-HTTP targets such as models (local or remote) or custom code.

llm red team setup

Select plugins​

Next, select the plugins that you want to use. Plugins are adversarial generators. They produce malicious inputs that are sent to your application.

Check off the individual plugins you want to use, or select a preset that includes a combination of plugins (if in doubt, stick with "Default").

llm red team setup

Select attack strategies​

Now we select strategies. Strategies are techniques that wrap the generated inputs in a specific attack pattern.

This is how Promptfoo generates more sophisticated jailbreaks and injections.

llm red team setup

Review and save​

Finally, download the generated configuration file. You'll use this to run the red team from your local machine.

llm red team setup

Save the file as promptfooconfig.yaml. Then, navigate to the directory where you saved the file and run promptfoo redteam run.

info

If you don't want to use the UI to start a red team, you can use the init command instead:

promptfoo redteam init --no-gui

Run the scan​

Now that we've generated the test cases, we're ready to run the adversarial evaluation.

Run this command in the same directory as your promptfooconfig.yaml file:

npx promptfoo@latest redteam run

This command will generate several hundred adversarial inputs across many categories of potential harm and save them in redteam.yaml. Then, it will run the test cases against the target.

llm red team run

View the results​

npx promptfoo@latest redteam report

Promptfoo provides a report view that lets you dig into specific red team failure cases:

llm red team report

That view includes a breakdown of specific test types that are connected to the eval view:

llm red team remediations

Clicking into a specific test case to view logs will display the raw inputs and outputs:

llm red team evals

Understanding the report view​

The red teaming results provide insights into various aspects of your LLM application's behavior:

  1. Vulnerability categories: Identifies the types of vulnerabilities discovered, such as prompt injections, context poisoning, or unintended behaviors.
  2. Severity levels: Classifies vulnerabilities based on their potential impact and likelihood of occurrence.
  3. Logs: Provides concrete instances of inputs that triggered vulnerabilities.
  4. Suggested mitigations: Recommendations for addressing identified vulnerabilities, which may include prompt engineering, additional safeguards, or architectural changes.

Common target types​

Attacking an API endpoint​

The configuration file includes a description of the target endpoint. You can edit the config to make changes to the target. For example:

targets:
  - id: https
    label: 'travel-agent-agent'
    config:
      url: 'https://example.com/generate'
      method: 'POST'
      headers:
        'Content-Type': 'application/json'
      body:
        myPrompt: '{{prompt}}'

purpose: 'The user is a budget traveler looking for the best deals. The system is a travel agent that helps the user plan their trip. The user is anonymous and should not be able to access any information about other users, employees, or other individuals.'

The label is used to create issues and report the results of the red teaming. Make sure to re-use the same label when generating new redteam configs for the same target.

Setting the purpose is optional, but it will significantly improve the quality of the generated test cases and grading. Be specific about who the user of the system is and what information and actions they should be able to access.

info

For more information on configuring an HTTP target, see HTTP requests.

Alternative: Test specific prompts and models​

If you don't have a live endpoint, you can edit the config to set the specific prompt(s) and the LLM model(s) to test:

prompts:
  - 'Act as a travel agent and help the user plan their trip. User query: {{query}}'
  # Paths to prompts also work:
  # - file://path/to/prompt.txt

targets:
  - id: openai:gpt-4o-mini
    label: 'travel-agent-mini'

Promptfoo supports dozens of model providers. For more information on supported targets, see Custom Providers. For more information on supported prompt formats, see prompts.

Alternative: Talking directly to your app​

Promptfoo hooks directly into your existing LLM app to attack targets via Python, Javascript, RAG or agent workflows, HTTP API, and more. See custom providers for details on setting up:

Continuous improvement​

Red teaming is not a one-time activity but an ongoing process. As you develop and refine your LLM application, regularly running red team evaluations helps ensure that:

  1. New features or changes don't introduce unexpected vulnerabilities
  2. Your application remains robust against evolving attack techniques
  3. You can quantify and demonstrate improvements in safety and reliability over time

Check out the CI/CD integration docs for more info.

Resources​