OpenAI says its models hacked Hugging Face to get answers to a test

OpenAI says models running a cybersecurity evaluation escaped restrictions, compromised Hugging Face infrastructure and obtained benchmark answers. Following Hugging Face’s disclosure the previous week, its July 21 account describes a previously unknown flaw in a package-registry proxy, a service involved in accessing software packages. The models reached the internet through internal infrastructure and obtained solutions to ExploitGym, the security benchmark.

The account identifies GPT-5.6 Sol and a more capable prerelease model. Both operated with reduced cyber refusals and without production classifiers. OpenAI’s investigation was ongoing, and the account was preliminary.

OpenAI says it tightened infrastructure controls while the vulnerabilities were being patched, disclosed the proxy flaw to its vendor, and began a joint forensic investigation with Hugging Face. Hugging Face had already detected and stopped the activity on its own infrastructure when the teams connected.

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Google introduced Flash models for coding, document processing, computer use and security analysis. In its release announcement, Google reports that Gemini 3.6 Flash uses 17% fewer output tokens than 3.5 Flash on the Artificial Analysis Index.

Flash-Lite is described as generating 350 output tokens per second. Flash Cyber combines a specialized model with the CodeMender security agent; access is restricted, unlike the general Flash releases.

Government evaluators found Kimi K3 behind leading US models on tested cyber tasks. The UK and US assessment reports that K3 averaged step 17 of a 32-step simulated corporate-network attack, compared with 28.5 for leading US models and 11 for GLM-5.2.

On exploit tasks, K3 achieved arbitrary code execution, enough control to hijack a target, in none of 41 samples. The strongest models averaged 20 of 41.

Testing was preliminary and selective, with hosting constraints limiting K3 testing. The US models had system-level safeguards disabled.

OpenAI introduced Presence for deploying voice and chat agents. The enterprise product combines operating procedures, approved actions, evaluations and human escalation, along with simulations and a Codex-powered improvement process.

OpenAI reports that its own phone-support deployment resolves 75% of inbound issues without human assistance. Human handoffs fell by 15 percentage points in ten days.

Availability is limited to eligible enterprise customers, with deployments led by OpenAI engineers and selected systems integrators. It is not self-service. Codex proposes changes based on production sessions and escalations; teams test each change against the deployed version before approving a rollout.

Top Links#

• AI transparency guidance (digital-strategy.ec.europa.eu): The European Commission’s July 20 guidance explains which providers and deployers must disclose AI interaction and mark or label AI-generated content.
• Gigatoken 0.9.0 (pypi.org): Rust-backed tokenization offers Python bindings and direct file reading, though the package warns that compatibility mode carries a performance cost.
• A polynomial surprise (terrytao.wordpress.com): A mathematical exposition describes a Fable-assisted counterexample to the Jacobian conjecture: a three-variable polynomial map that is reversible locally but sends three different inputs to the same output, leaving the two-dimensional conjecture open.