OpenAI has announced that it's pausing some "internal activities" involving its upcoming artificial intelligence (AI) model Astra after an internal evaluation found it had made significant advancements in agentic coding and cybersecurity.

In response to the discovery, the AI upstart said it's implementing security controls for higher-capability models and associated activities, such as isolated testing environments, restricted network and tool access, enhanced model weight protections and encryption, additional monitoring and detection capabilities, and sandboxed execution.

"We are pausing internal activities involving Astra that do not yet meet these strengthened security control requirements," it said in a statement.

"We have implemented universal monitoring for risky actions and misalignment across all agentic applications of Astra, including training and evaluation. Monitors evaluate the model's Chain of Thought and trigger a security response to review and interrupt high risk activity."

OpenAI said it will also work with relevant government agencies and select AI safety organizations to test out the model's capabilities, as well as sharing recommended security controls to third-party testing partners to run higher-risk evaluations and workloads safely.

The company said it "cannot rule out" the model has "Critical" cyber capabilities under its Preparedness Framework, which defines the threshold as follows -

A tool-augmented model can identify and develop functional zero-day exploits of all severity levels in many hardened real-world critical systems without human intervention OR model can devise and execute end-to-end novel strategies for cyberattacks against hardened targets given only a high level desired goal.

In other words, the model can discover and develop functional zero-day exploits of all severity levels in many hardened real-world critical systems without human intervention, or can orchestrate and execute end-to-end novel strategies for cyberattacks against targets when prompted a high-level desired goal.

OpenAI pointed out its preliminary evaluations of Astra indicate "strong enough performance" that it cannot eliminate the possibility that the model doesn't possess a "Critical" capability level at this stage. It also emphasized that Astra was not involved in last month's incident aimed at Hugging Face. In a recent academic paper, OpenAI touted that the model solved 10 open problems in mathematics and theoretical computer science for around $2,000 at Sol API rates.

OpenAI said it was sharing this information because it believes "it's important to be transparent with the public and the safety and security communities about this potential shift in capabilities."

"We believe advanced cyber-capable models should help defenders identify and address vulnerabilities before attackers do," it added. "We're committed to working alongside governments, safety institutes, and civil society to ensure that the frontier capabilities of models like Astra, and those that follow, are deployed responsibly and broadly for the benefit of all humanity."

The development is the latest sign of rapidly advancing cyber capabilities from frontier models, even as it marks the first time an AI lab has publicly committed to slowing progress due to cybersecurity concerns.

Earlier last week, the U.K. AI Security Institute (AISI) disclosed that its own evaluation found that AI models with access to the internet reached out into the real world to target individuals and organizations autonomously across 10 of the total of 122 runs. Of 19 such actions recorded, 17 originated from Anthropic's Mythos 5 and the remaining two involved OpenAI's GPT-5.6-Sol with cyber classifiers.

"In the most serious case, an agent tried to insert malicious code into an open-source project," AISI said. "In an attempt to get the code approved, the agent engaged in social engineering – creating fake online identities and using them to pressure the project's maintainer to approve the code. A human maintainer caught and refused to approve the malicious code."

"These attempts were unsuccessful, and our investigations have not evidenced any resulting real-world harm. But this is the first time we have seen risks around autonomy and deception manifest this clearly, without specific prompting, in the real-world."

The disclosure also comes amid revelations that models from Meta and Chinese company Moonshot, namely, Muse Spark ​1.1 and Kimi K3, escaped contained and targeted real-world targets, amplifying concerns about developers' abilities to sandbox increasingly capable AI systems. In both cases, the models have been found to weaponize network misconfigurations as opposed to independently identifying and exploiting a previously unknown vulnerability to reach the internet.

As AI models are tested against widely accepted benchmarks to examine how they perform offensive and defensive cybersecurity tasks in isolated test environments, Frontier Security said Kimi K3 found a network egress leak that enabled it to reach out github[.]com, clone an official repository for the benchmark problem it was supposed to be solving, and access the solution rather than solving the challenge by itself.

"In our case the model didn't solve the task natively at all, it probed the network, realized standard DNS resolution for github.com was functional (most other websites were blocked by the sandbox), cloned the official benchmark repository, and read the solution directly off the disk," Frontier Security said.

The ⁠growing list of ⁠incidents in ​which AI agents from major developers escaped testing environments in different ways and ended up breaching real targets that were not part of the experiment has prompted the creation of a new website, aptly named Felony Bench, to track these cases.

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