GPT-Red: Unlocking Self-Improvement for Robustness

AI systems commonly encounter third-party data through browsers, connected apps, local files, and other tools. These affordances are necessary for performing real-world tasks, but they also create more opportunities for malicious actors to influence model behavior. For example, a third party might embed a carefully crafted instruction ⁠ —designed to trick the model into uploading sensitive data to an external server—in an email, webpage, tool response, or code repository.

Human red-teaming is a critical part of our safety work, helping us uncover these vulnerabilities before deployment and put the right safeguards in place. But human red-teaming alone is difficult to scale. Designing and running these exercises is time-intensive, limiting how quickly we can identify new failure modes and incorporate them into stronger safeguards. Further, while these exercises produce valuable examples of successful attacks, they cannot generate the volume and diversity of adversarial data needed to improve model robustness through training.

Keeping pace with increasingly capable models requires red-teaming to scale as well. To this end, we have been training automated, internal-only red-teaming models that uncover vulnerabilities before deployment and generate attacks during model training to improve robustness. We believe automated red-teaming unlocks a crucial form of self-improvement for safety: using today’s models to directly help make future models safer.

GPT‑Red is trained using self-play reinforcement learning, where the model and a collection of diverse defender LLMs are trained simultaneously on a broad set of red-teaming scenarios. GPT‑Red is rewarded for eliciting a valid failure, such as a successful prompt injection, while the defender models are rewarded for resisting the attack and completing their original tasks. As the defenders become more robust, GPT‑Red is forced to discover stronger and more diverse attacks.

The ultimate test of a red-teamer is the ability to achieve targeted malicious goals against real-world agentic systems with incomplete knowledge of the system’s underlying model and harness design. Our first experiment in this setting pitted GPT‑Red against an AI-powered vending machine in the OpenAI office (similar to Project Vend ⁠ (opens in a new window) ) produced by Andon Labs. We gave GPT‑Red a description of the system and the ability to send attacks and observe tool calls from the simulated agent that closely mirrors the real world deployment. After iterating on attacks, GPT‑Red deployed its attack against the production agent, achieving all three of its malicious objectives:

To support self-play training, we build an expansive set of realistic scenarios where prompt injections might be inserted. Each environment has a threat model that specifies what GPT‑Red can control and what counts as a successful attack. For instance, GPT‑Red might control part of a local file, a webpage banner, an email body, or output of a tool.

GPT‑Red is the culmination of these efforts and our current best automated safety red-teaming model. Similar to how human red-teamers craft attacks, the model works toward a goal by sending a prompt, observing how GPT models respond to it, and iterating. We trained GPT‑Red at the compute scale of some of our largest post-training runs at OpenAI—an unprecedented amount of compute dedicated purely for improving safety.

We directly incorporate GPT‑Red into the training process of our production models. As a result, GPT‑5.6 Sol is our most robust model to prompt injections to date, achieving 6x fewer failures on our hardest direct prompt injection benchmark compared to our best production model from just four months earlier. The scalability of our approach leaves us excited for even stronger results in the future as we continue to train stronger red-teamers.

At the end of its training, GPT‑Red is a very strong attacker: it can break nearly all models it is pitted against, both internal and production models up to and including GPT‑5.5. After GPT‑Red completed training, we used it to generate prompt injections for the training of GPT‑5.6, resulting in the model becoming highly resistant to GPT‑Red’s attacks.