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Automated red-team discovery for AI models

Project description

outofthebox

Automated red-team discovery for AI models. Generates probe prompts, sends them to a target model, judges responses against your objective, and iteratively refines the best-scoring probes.

Install

pipx install probegpt

Usage

probegpt

Full-screen TUI. Set up your models in Config, then go to Run Discovery, enter your objective, and hit Run.

Models

Supports two providers per role (target / generator / judge):

  • Azure OpenAI — uses az login credentials automatically
  • OpenRouter — set OPENROUTER_API_KEY in a .env file or your environment

Config is saved to ~/.outofthebox/config.json.

How it works

  1. Loads 9 seed probes covering known red-team techniques
  2. Iteration 1 — generator writes N probes targeting your objective
  3. Iteration 2+ — top-scoring probes from the previous round seed mutation + fresh objective variants
  4. Each probe is sent to the target model; the judge scores the response against your objective (0.0 → 1.0)
  5. Results optionally exported to JSON

Requirements

  • Python 3.11+
  • For Azure: az login with access to your deployment
  • For OpenRouter: OPENROUTER_API_KEY env var

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