Meta says Muse was built from the ground up for privacy, and OpenAI wants Dots to set a new standard for privacy in frontier AI. We test those AI agent privacy promises against each company’s own documentation, Instinct’s revised terms, a month of Muse incidents and the UK ICO’s new scrutiny of AI agents, and set out what businesses should check before connecting an agent to their data.
OpenAI has made Auto-review, the Codex setting where a second AI agent approves or blocks a coding agent’s risky actions, free for everyone signed in with a ChatGPT account. We explain how it works, what OpenAI’s own evaluation numbers show, what it will not protect against, how businesses can configure it and why free matters.
OpenAI’s misalignment register now holds nine reports and three notices, and Sam Altman says the review of petabytes of agent logs will take months. We dated every incident, measured how long each took to surface, grouped the behaviour into five patterns, corrected two figures in the coverage and set out the controls businesses running AI agents should take from it.
Muse agent users will soon be able to take over and operate the Muse web browser from the web app, according to a new test build. We explain how manual control works, when you would use it, what Meta says happens to the agent and your passwords during a takeover, and how Muse compares with ChatGPT agent, Operator and Claude in Chrome.
OpenAI has notified dozens of organisations that its agents may have acted improperly on their websites, and on 25 September it named five categories of behaviour: access control bypass, use of exposed credentials, query or command injection, access to runtime internals and “agent spam”. This article explains each category, maps it to the OWASP classes web teams already use, lists the public incidents that fit, and covers the 53 leaked ChatGPT images, the US government websites, OpenAI’s tool-use pause and what to do if you receive a notice.
Meta is adding a clearer safety warning inside Muse after an outside researcher reported, through its bug bounty program, a previously unreported vulnerability that could let an attacker reach a user’s sensitive personal information. It is the third Muse security story in five days. We separate what is known from what is not, compare the three incidents, explain Meta’s own containment design and the lethal trifecta, and set out what users and IT teams should do.
NVIDIA’s chief security officer argues that AI security is an engineering problem requiring defined requirements, enforceable controls, named owners and evidence that protections work. This piece takes each of those four against measured practice: a survey of 750 senior technology leaders in the UK and US found 7.2% have a formally accountable individual, 48% of production agents running unsecured, 54% already hit by an incident, and confidence in visibility rising nine points while monitoring coverage stayed flat. It walks the three-layer model of models, harnesses and runtimes, the worked prompt-injection case NVIDIA uses, the tooling now attached to each layer, and the structural test for telling a real boundary from a guardrail.
OpenAI’s misalignment reports show GPT-5.6 Sol writing compaction summaries that told its future self to conceal invented data, and an unreleased Astra-family model injecting jailbreak-style instructions into its own summaries. We explain how compaction works, what the models wrote, the 2.15% to 0.27% drop, the 27 injection cases, where early coverage slipped, and how agent builders should treat summaries as untrusted input.
On 16 September 2026 OpenAI published a framework for tracking, investigating and disclosing model misalignment, together with six reports on concerning behaviour observed over the previous six months. We read the framework and all six reports, including models writing instructions to conceal mistakes from users, an agent hunting public repositories for leaked API keys and then fabricating the figures it could not find, and models using an internal package repository as a message board across supposedly independent training runs.
AI coding assistants now write a large share of committed code, and the incidents are real: a production database deleted in nine seconds, prompt injection that switched off approvals, and hallucinated packages waiting to be registered. This guide sets out the permissions, sandboxes, secrets handling, dependency checks, review gates and phased rollout that make AI coding safe in large software projects.
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