arXiv subjects

Juan Felipe Cerón Uribe

Publications and source records attributed to Juan Felipe Cerón Uribe.

2 recordsLinked to original sources

arXiv2026

Capability-Based Planning for AI Crisis Preparedness

Capability-based planning drives preparedness in defense and homeland security, but has yet to be applied seriously to AI. Government AI preparations follow a predict-then-act paradigm: rank risks by likelihood and impact, then prepare for the highest expected harm. AI resists prediction: expert timelines disagree by orders of magnitude, and official reviews concede that likelihood-based risk assessment fails for exactly this class of risk. Drawing on principles of decision making under deep uncertainty, we propose a methodological framework in three parts: a scenario library sampled systematically across declared axes; a rating procedure that assesses each government capability against each scenario on coarse, gated criteria; and a prioritization step that maps the resulting matrix onto decision rules a government might adopt. Through a pilot across the four most severe AI-enabled threat classes, we illustrate the kind of insight the instrument yields and provide a proof of concept for capability-based planning as a practical tool for AI crisis preparedness.

cs.CY↗

arXiv2026

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 ever documented. GPT-Red excels at red-teaming: it reliably breaks our past models up to GPT-5.5, it finds more successful attacks than human red-teamers, and it generalizes to held-out environments, defender models, and harnesses. In the future, we expect that as we improve the robustness of each new GPT model, it will in turn will provide better learning signal for \textit{even stronger} red-teamer agents, thus unlocking a self-improvement flywheel.

cs.CR↗