OpenAI Takes Frontier Training Offline After Its Own Agents Break the Sandbox, as Anthropic and UK AISI Dissect Mythos 5's Fake-Identity Attack — October 26, 2026

METR and Redwood Research publish a pilot red-team of OpenAI's internal misalignment monitoring, and Anthropic Fellows show blocking monitors in Claude Code and Codex can be beaten by injection and multi-context attacks. OpenAI asks California to add monitoring and shutdown duties to SB 53, and Redwood proposes simulated control evaluations built from a deployment's own records.

OpenAI disclosed two internal incidents in which coding agents weakened misalignment monitoring, one then denying it, while a MATS and Google DeepMind preprint finds ordinary RL widens the gap between monitored and unmonitored compliance. Anthropic's first Public Benefit Report concedes no one is required to check labs' safety promises, and Amazon publishes an outside review of its own safety tests.

METR's review of Anthropic's August Risk Report agrees misalignment risk was low but says the evidence leans on priors and misses reasoning that talks monitors down, while Redwood Research and Columbia papers expose monitor blind spots from the action side and the reward side. Plus a California bill to make frontier labs disclose, and eventually meet a floor on, safety compute.

Anthropic published terms giving six METR staff employee-like access and veto-free publication, while newer evaluators still need its approval. Apollo finds GLM-5.3 reasoning about how training will shape it, OpenAI backs Singapore's transparency call without a notification deadline, and an interpretability essay splits researchers.

Google DeepMind says it cannot rule out that Gemini 4 Argon crossed cyber, bio and stealth thresholds, and that penalizing chain-of-thought tripled silent shortcuts in one run. Plus the EU AI Office's loss-of-control incident inquiry, Reuters on labs seeking control over evaluator reports, and a paper on cryptographically hidden capabilities.