OpenAI Is About to Release Its First AI Model With'Critical' Cyber Abilities The company will give select partners early access to its Astra AI model--so they have time to shore up their defenses. OpenAI announced Tuesday that its forthcoming AI model, Astra, is its first to reach the company's threshold for what it calls "critical" cyber capabilities. OpenAI says it plans to publicly release a version of Astra "soon," but will make the model's advanced cyber capabilities available only to select partners in its Daybreak Blue early-access program at launch. In a briefing with reporters, OpenAI safety and security leaders said the company has concluded that Astra reaches the critical cybersecurity capabilities outlined in its preparedness framework, which sets thresholds and protocols for when its AI models pose new levels of risk. The company says an AI model has reached its critical cyber threshold when it can independently find and exploit previously unknown vulnerabilities in real-world software.

OpenAI is planning to cut off Cursor's access from its models, following the AI coding assistant's acquisition by SpaceXAI. In a post on its website, the company said it's giving Cursor users as much time as it could based on its contract. It intends to pull its models from the service on November 12, 2026, but it said that Cursor could choose an earlier termination date. The company explained that it's making this choice because it can't be confident that SpaceX will use its technology within its terms of services, based on its "experience with Elon Musk's companies violating contracts." OpenAI and Musk have a long and troubled history, with the SpaceXAI chief being one of the company's earliest investors.

Amid heated race with Anthropic, firm says it's overhauling research and training and will require greater safety parameters of AI after hack OpenAI on Tuesday said it had slowed down the pace of its AI development while it overhauled its research and training systems. The company's researchers were caught unaware last month when an AI agent under testing hacked another AI firm, Hugging Face. The AI research lab behind ChatGPT said its new measures included pausing its model testing for two weeks and investing more in adding other AI systems to monitor the activities of AI agents in testing. Some of the company's largest planned training runs remain on hold, the company said. The first anti-AI protester to be jailed has a message for OpenAI, Anthropic and Meta: 'Regain your humanity' The company did not reply to questions about when the slowdown began or when it planned to return to its normal pace of development.

OpenAI is giving some members of its Daybreak cybersecurity program access to a new model that's less likely to refuse higher-risk tasks. The company is also expanding access to Daybreak to more partners, including Accenture, IBM, CrowdStrike, Cisco, Sophos and Cloudflare. OpenAI says the companies will use the cyber models available through Daybreak to protect their customers. Under the expanded program, Daybreak is available to partners in two tiers. Daybreak Blue gives them access to frontier general-purpose models, including GPT‑5.6 Sol, OpenAI's most advanced one yet.

Shortly after a major cybersecurity incident where OpenAI's models hacked into an open source machine learning platform called Hugging Face, the company announced that it's bolstering safeguards and security controls for its latest AI model. In a post on its website, OpenAI said internal evaluations of its upcoming model, called Astra, showed "significant advancements in agentic coding and cybersecurity," resulting in OpenAI not being able to "rule out critical cyber capabilities." According to OpenAI, it can't declare with certainty that the unreleased Astra model would be designated as a "Critical capability level." As detailed in its own Preparedness Framework, OpenAI said the Critical designation means that a model "can identify and develop functional zero-day exploits of all severity levels in many hardened real-world critical systems without human intervention." It could also be able to "devise and execute end-to-end novel strategies for cyberattacks against hardened targets given only a high level desired goal."

When you purchase through links in our articles, we may earn a small commission. The unreleased Astra model may possess "critical" cybersecurity abilities, OpenAI warns. Less than a week after touting the scientific achievements of Astra, its next "major" model, OpenAI says it's "pausing internal activities" related to the model due to its powerful cybersecurity abilities. "Our latest internal evaluations of Astra, one of our upcoming models, over the past few days indicate significant advancements in agentic coding and cybersecurity," OpenAI stated in a Friday press release . "These results, in addition to expert assessments, have led us to conclude last night that we cannot rule out critical cyber capabilities under our Preparedness Framework ."

Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series Everything we know about the quantum math-solving model. OpenAI confirmed the existence of Astra, a smarter, unreleased model that's already achieving big results in the math world. Timothy Beck Werth is the Tech Editor at Mashable, where he leads coverage and assignments for the Tech and Shopping verticals. Tim has over 15 years of experience as a journalist and editor, and he has particular experience covering and testing consumer technology, smart home gadgets, and men's grooming and style products. Previously, he was the Managing Editor and then Site Director of SPY.com, a men's product review and lifestyle website.

Training data attribution (TDA) provides insights into which training data is responsible for a learned model behavior. Gradient-based TDA methods such as influence functions and unrolled differentiation both involve a computation that resembles an inverse Hessian-vector product (iHVP), which is difficult to approximate efficiently. We introduce an algorithm (ASTRA) which uses the EKFAC-preconditioner on Neumann series iterations to arrive at an accurate iHVP approximation for TDA. ASTRA is easy to tune, requires fewer iterations than Neumann series iterations, and is more accurate than EKFAC-based approximations. Using ASTRA, we show that improving the accuracy of the iHVP approximation can significantly improve TDA performance.

Training data attribution (TDA) provides insights into which training data is responsible for a learned model behavior. Gradient-based TDA methods such as influence functions and unrolled differentiation both involve a computation that resembles an inverse Hessian-vector product (iHVP), which is difficult to approximate efficiently. We introduce an algorithm (ASTRA) which uses the EKFAC-preconditioner on Neumann series iterations to arrive at an accurate iHVP approximation for TDA. ASTRA is easy to tune, requires fewer iterations than Neumann series iterations, and is more accurate than EKFAC-based approximations. Using ASTRA, we show that improving the accuracy of the iHVP approximation can significantly improve TDA performance.

GPU kernel optimization has long been a central challenge at the intersection of high-performance computing and machine learning. Efficient kernels are crucial for accelerating large language model (LLM) training and serving, yet attaining high performance typically requires extensive manual tuning. Compiler-based systems reduce some of this burden, but still demand substantial manual design and engineering effort. Recently, researchers have explored using LLMs for GPU kernel generation, though prior work has largely focused on translating high-level PyTorch modules into CUDA code. In this work, we introduce Astra, the first LLM-based multi-agent system for GPU kernel optimization. Unlike previous approaches, Astra starts from existing CUDA implementations extracted from SGLang, a widely deployed framework for serving LLMs, rather than treating PyTorch modules as the specification. Within Astra, specialized LLM agents collaborate through iterative code generation, testing, profiling, and planning to produce kernels that are both correct and high-performance. On kernels from SGLang, Astra achieves an average speedup of 1.32x using zero-shot prompting with OpenAI o4-mini. A detailed case study further demonstrates that LLMs can autonomously apply loop transformations, optimize memory access patterns, exploit CUDA intrinsics, and leverage fast math operations to yield substantial performance gains. Our work highlights multi-agent LLM systems as a promising new paradigm for GPU kernel optimization. Our code is publicly available at https://github.com/Anjiang-Wei/Astra.