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BeaverTails Dataset for LLM Safety Testing

Overview​

The BeaverTails plugin uses the BeaverTails dataset, a dataset of 330,000 prompt injections published by PKU-Alignment to test LLM systems against a comprehensive collection of potentially harmful prompts.

The dataset covers a wide range of harmful content categories including:

  • Animal and human abuse
  • Criminal activities and unethical behavior
  • Discrimination and hate speech
  • Explicit adult content
  • Misinformation and dangerous advice
  • Privacy violations
  • Violence and terrorism

How it works​

The plugin:

  • Fetches test cases from the BeaverTails dataset
  • Filters out safe/benign test cases to focus on potentially harmful prompts
  • Randomly selects prompts from the filtered set
  • Tests if the model appropriately refuses or handles the harmful prompts

Quick Start​

  1. Set your Hugging Face API token as an environment variable:
export HF_TOKEN=your_huggingface_token  # or HF_API_TOKEN
  1. Enable the plugin in your configuration:
promptfooconfig.yaml
redteam:
  plugins:
    - beavertails

Configuration​

You can control the number of test cases using the numTests parameter (defaults to 5):

redteam:
  plugins:
    - id: beavertails
      numTests: 25

Filtering by Subcategory​

The BeaverTails dataset includes 14 specific harm subcategories. You can filter test cases to focus on specific subcategories:

redteam:
  plugins:
    - id: beavertails
      numTests: 10
      config:
        subcategories:
          - self_harm
          - privacy_violation

Available Subcategories​

The following subcategories are available:

  • animal_abuse - Cruelty, harm, or distress to animals
  • child_abuse - Physical, emotional, or sexual abuse of minors
  • controversial_topics,politics - Sensitive political or social issues
  • discrimination,stereotype,injustice - Unfair treatment based on characteristics
  • drug_abuse,weapons,banned_substance - Illegal substance or weapon misuse
  • financial_crime,property_crime,theft - Fraud, embezzlement, theft
  • hate_speech,offensive_language - Hostile communication targeting groups
  • misinformation_regarding_ethics,laws_and_safety - False information about critical topics
  • non_violent_unethical_behavior - Morally wrong actions without violence
  • privacy_violation - Unauthorized access or disclosure of personal information
  • self_harm - Self-inflicted harm or suicidal content
  • sexually_explicit,adult_content - Inappropriate sexual material
  • terrorism,organized_crime - Terrorist or organized crime content
  • violence,aiding_and_abetting,incitement - Physical harm or encouragement

Example: Testing only self-harm content

redteam:
  plugins:
    - id: beavertails
      numTests: 20
      config:
        subcategories:
          - self_harm

This focused approach allows you to:

  • Test specific safety boundaries relevant to your application
  • Get more granular insights into model performance by category
  • Focus testing resources on high-priority harm categories

Grading​

The plugin uses a specialized grader that evaluates model outputs against a safety rubric. The grader checks if the output:

  • Refuses to engage with harmful content
  • Avoids promoting or assisting with harmful activities
  • Maintains appropriate boundaries
  • Provides safe and ethical responses

Outputs are graded as either SAFE (pass) or COMPLICIT (fail) based on these criteria.