Security incident disclosure — July 2026

2024 Security Feature Highlights

An update on our safety & security practices

An update on our safety & security practices

Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident

Space secrets security update

Election information and safeguards in 2026

Ahead of global elections, we’re helping people access information, supporting cyber defenders, and increasing AI transparency

Future-Back Threat Modeling: A Foresight-Driven Security Framework

Future-Back Threat Modeling (FBTM) is a predictive security framework that starts with envisioned future threat states and works backward to uncover assumptions, gaps, blind spots, and vulnerabilities in current defense architectures. It aims to reveal both known unknowns and unknown unknowns, including emerging tactics, techniques, and procedures, thereby improving the predictability of adversary behavior under future uncertainty. By anticipating future threats such as AI, information warfare, and supply chain attacks, FBTM helps security leaders make informed decisions today to build more resilient security postures for the future.

Information Leakage Detection through Approximate Bayes-optimal Prediction

arXiv:2401. 14283v4 Announce Type: replace-cross Abstract: In today's data-driven world, the proliferation of publicly available information raises security concerns due to the information leakage (IL) problem.

Towards an Automated Test of LLM Security Knowledge

arXiv:2607. 18496v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for a range of software, hardware and human-centered security tasks.

Confidential submission of draft S-1 to the SEC

OpenAI confirms a confidential S-1 submission to the SEC and has not yet determined timing for further action.

Privacy-Preserving AI Verification via Minimal Information Disclosure

arXiv:2608. 02774v1 Announce Type: cross Abstract: AI verification crosses a trust boundary: a verifier must learn enough to establish an authorized claim, yet the same evidence can reveal sensitive details about the model, workload, or hardware.

(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations

arXiv:2607. 26201v1 Announce Type: cross Abstract: Security operations centers rely on anomaly detection systems to flag suspicious events.

Protecting patient privacy in clinical foundation models: Technical and legal perspectives

arXiv:2608. 07705v1 Announce Type: new Abstract: Clinical foundation models trained on large-scale patient data are increasingly used for decision support, screening, and public health.