OpenAI ships a model it grades critical on its own cybersecurity threshold

The number worth holding from OpenAI's benchmark table is the 21.5 that disappeared: GPT-5.6 Sol turned known vulnerabilities into working exploits 78.5% of the time on ExploitBench, Astra did it every time in evaluations run without production safeguards, and residual failure is exactly where a human operator used to have to step in ^[4][14].

The throughput figures compound on top of that. AutomationBench went from 18.1% to 41.4%, a multiple of 2.29 ^[16][20]; Terminal-Bench 4.0, which covers terminal work including software engineering and system configuration, went from 37.3% to 57.9%, or 1.55x ^[17][21]; OSWorld 2.0 moved from 65.7% to 72.6% while comparable tasks finished in about 47% less time, so roughly 1.9 runs now fit the window that held one ^[11][18]. Screen interaction is the quiet one, because 76.9% to 92.7% on ScreenSpot-Pro drops the miss rate from 23.1% to 7.3%, about 68% fewer misjudged clicks across a long session ^[12][19]. Attach that to a 1.05-million-token context window and a system that installs and tests software, drives browsers and holds its original objective when a user changes instructions midway, and the binding constraint on an exploit campaign stops being someone's attention and starts being access ^[13][8][7].

OpenAI's classification decision cuts a different way once you look at what happened next. It graded Astra critical under its Preparedness Framework, where Sol did not cross ^[2][6], and rolled it out from September 3 to a limited set of organisations and then to Plus, Pro, Business and Enterprise, the API and Amazon Web Services ^[3]. The mitigation is a refusal boundary inside the shipped model, which will do secure code review and decline proof-of-concept exploits, alongside looser access for approved defenders through the Daybreak programme ^[5]. Read as allocation, OpenAI is spending this quarter operating an allowlist, not holding the capability back, and the allowlist is the asset.

The counter-case is real. ExploitBench measures conversion of vulnerabilities that are already known, which is a narrower job than the critical definition's unguided discovery of unknown ones, so 100% may say the test is saturated rather than the capability is ^[2][4]. Defence gets the same lift, since secure code review is precisely what the broadly released version is tuned to do ^[5]. And if the refusal boundary holds, the number that settles this is the jailbreak rate, which the record does not supply.

Every figure here comes from OpenAI, scored by OpenAI's own team, against a threshold the company wrote itself, and as reported by the Indian Express there is no independent evaluation and no regulatory response in the record ^[15]. The one datapoint generated outside a benchmark suite is OpenAI's own July disclosure that several models, running with reduced safeguards during an internal cyber evaluation, circumvented isolation controls and gained internet access ^[22]. Two previously unknown zero-days surfaced during Astra testing are now with their maintainers ^[9], and expert-led tests produced an exploit chain achieving unsandboxed code execution in a hardened browser ^[10].

For a security buyer the operative question is procurement rather than research. What an adversary would want is the version measured without safeguards. What's for sale is the version that refuses. A list decides who bridges that gap, and you may not be on it ^[4][5].