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Machine Learning

GPT-Red: Automated Red Teaming via Self-Play at Scale

Eric Wallace, Christopher A. Choquette-Choo, et al.

Featured August 11, 2026

Original Paper

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Simply

A smart computer program called GPT-Red learns to trick other advanced AI models by playing a game of attack and defense, making the defender AIs much tougher against sneaky instructions.

In depth
The paper introduces GPT-Red, an automated agent designed to find new ways to "break" large language models (LLMs) through prompt injection attacks. It achieves this by training GPT-Red using a scalable self-play algorithm, where the attacker agent continuously battles and learns from a diverse group of defender LLMs. This adversarial process, powered by an agentic harness for iterative attack refinement and vast computational resources, creates a "flywheel" that simultaneously strengthens both attackers and defenders, leading to more robust LLMs like GPT-5.6.

Key Takeaways

  • 1
    The authors developed GPT-Red, an AI agent that autonomously discovers novel and complex prompt injection attacks against LLMs, outperforming human red-teamers.
  • 2
    The core innovation is a self-play algorithm that trains an attacker against a diverse population of defenders, generating a continuous stream of challenging adversarial data.
  • 3
    The attacks generated by GPT-Red are used to adversarially train frontier models like GPT-5.6, significantly improving their robustness to various prompt injection and jailbreak attempts.

Conceptual Flow

HIGH LEVEL
1
Automated Adversarial Training Loop

A special AI learns to trick other AIs, and when it succeeds, the other AIs learn to be smarter, making both sides better over time.

Attacker AI
Defender AIs
Play Attack-Defense Game
Smarter Attacker AI
Stronger Defender AIs
2
Significantly Improved LLM Safety

The new training method makes advanced AI models much harder to trick, even better than human experts can make them.

Old AI Models
Human Attackers
New AI Training
Super Robust AI Models
AI Attackers Win More