GPT-Red: Automated Red Teaming via Self-Play at Scale
We introduce \textbf{GPT-Red}, an automated red-teaming agent that is trained to discover novel prompt injection attacks against frontier LLMs. The goal of this model is to evaluate and improve the robustness of our production systems. To this end, we use it to adversarially train GPT-5.6, our most robust model to prompt injections to date. To create GPT-Red, we design a scalable self-play algorithm where the model is tasked with attacking a diverse population of simultaneously-trained defender agents. We train the model on realistic red-teaming environments using compute on the same scale as some of our largest RL post-training runs, making it the single-largest LLM safety training run e...
| Authors | Eric Wallace, Christopher A. Choquette-Choo, Nikhil Kandpal et al. |
|---|---|
| Primary category | cs.CR |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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