OpenAI has introduced its latest cybersecurity-specific model, GPT-5.6-Cyber, and the Daybreak program, in response to the rapidly narrowing window for cyber defense as threat actors increasingly leverage AI for attacks. The program aims to equip approved defenders with frontier AI capabilities.

Daybreak offers two access tiers: Daybreak Blue provides access to frontier general-purpose models, including GPT-5.6 Sol, with safeguards tailored for authorized defensive security work such as vulnerability discovery and secure code review. Daybreak Red offers access to purpose-trained cybersecurity models like GPT-5.6-Cyber for authorized vulnerability research and exploit validation.

GPT-5.6-Cyber, built on GPT-5.6 Sol, is specifically trained to enhance capabilities in specialized cybersecurity tasks, such as finding zero-day vulnerabilities and developing exploit chains, while significantly reducing refusals for certain higher-risk cyber tasks. Internal evaluations show GPT-5.6-Cyber completing 95.0% of advanced cybersecurity requests, compared to just 1.5% for GPT-5.6 Sol.

The model has demonstrated its effectiveness in real-world vulnerability research, uncovering two previously unknown vulnerabilities in the V8 JavaScript engine (CVE-2026-15903) and reporting them to Google. It has also identified over 400 high-severity issues across popular mobile operating systems, databases, and OS kernels. OpenAI is implementing additional safeguards, including mandatory hardware security keys and enhanced monitoring, to ensure the safe use of these models.

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The article by Will Knight, titled "A New Trick Reveals AI Models’ Inner Thoughts," focuses on novel research and techniques addressing the "black box" problem of how AI models, particularly Large Language Models (LLMs), make decisions and generate outputs.

This "new trick" likely involves innovative methods to probe the internal representations of AI, visualize their reasoning processes, and identify decision-making pathways, thereby offering deeper insights into how models arrive at their conclusions. Such advancements are expected to enhance AI trustworthiness, explainability, and debugging capabilities.

The article suggests that these groundbreaking approaches to unveiling AI's "thoughts" represent a significant step forward in AI research, paving the way for greater transparency and understanding of artificial intelligence.

👉 Read the full article on Wired

A research article from Anthropic details a groundbreaking advancement made by an unreleased research version of Claude concerning one of mathematics' most famous unsolved problems, the Riemann hypothesis. Specifically, Claude improved a longstanding lower bound for the fraction of zeros of the Riemann zeta function that satisfy the Riemann hypothesis, increasing it from 41.6% to 67.2%.

This achievement, though not a proof of the Riemann hypothesis itself, emerged unexpectedly during Claude's attempt to tackle the problem. Claude arrived at this finding through an intensive computational process, coordinating approximately 60 subagents, executing 2,400 shell commands, and writing hundreds of Python scripts. This process involved thousands of numerical checks and peer-review among the subagents.

Mathematicians at Anthropic validated Claude's paper, clarifying how its results integrate with prior mathematical research, particularly the works of Baluyot, Goldston, Suriajaya, Turnage-Butterbaugh, and Bombieri. This discovery serves as the latest example of the rapid progress in AI models' mathematical capabilities, demonstrating their potential to extend the reach of mathematicians' ideas.

Initially, Claude itself was skeptical of its own finding, but through encouraging prompts, it ultimately produced the result. This suggests that AI models might underestimate their own capabilities, potentially mirroring how humans might underestimate the pace of AI progress.

👉 Read the full article on Anthropic