Microsoft took down the Exchange Admin Center (EAC) today after a code change left administrators facing an “Internal Server Error” when trying to access the service. Microsoft first admitted that something was amiss at 0937 UTC on October 12, noting, “When attempting to access the Exchange admin center, affected admins may encounter an ‘Internal Server Error’ message.” Any administrator attempting to access the EAC could be affected. It was more than an hour before the company stated: “We’ve identified unexpected 302 redirect errors during affected Exchange admin center (EAC) access attempts and are analyzing diagnostic logs to isolate the source of the access failures. “While we continue our investigation, admins remain unable to access the EAC, with no workaround currently available.” So, a fun morning for Microsoft’s European users. According to Microsoft, the EAC is “a modern, web-based management console for managing Exchange Online.” Want to manage mailboxes? EAC is where you go. Group management? Again, EAC. Microsoft explained: “The design of the EAC in Exchange Online aligns with the overall Microsoft 365 admin experience.” Sadly, that alignment appears to extend to service wobbles caused by changes rolled out without due care and attention. Two hours after admitting things had gone awry, Microsoft said it was “continuing to test the rollback of the recent Exchange admin center (EAC) change while monitoring the results to determine if this resolves the impact.” At this point, administrators were nodding sagely at an all-too familiar story. Of course rolling back the change did the trick. Microsoft stated as much in its final update: “A recent EAC configuration change resulted in unexpected HTTP responses within the affected request path, preventing admins from successfully accessing the EAC. “Reverting the identified change restored expected service functionality.” Microsoft also assured customers it was reviewing the change to ensure it didn’t make the same mistake again, as well as its monitoring and telemetry, to “improve detection and alerting for similar service behavior.” The problem is that this is hardly a new phenomenon. In September, Microsoft made a configuration change that left some users unable to access SharePoint Online sites and pages. In April, another ill-thought-through change took down Outlook for iOS. And, in 2025, Microsoft 365 tumbled from the cloud because of yet another configuration change. It’s almost as if testing changes consists of little more than asking Copilot, “does this look alright to you?” and then hitting the go button. The latest outage was resolved before the end of the working day, and a preliminary Post-Incident Report is due within two business days, with a final report due next week. Hopefully one or the other will contain words along the lines of “Hey, this keeps happening, maybe we should do some more testing before deploying to production?” ®

If you thought everyone was into chatbots, think again: about half of US adults never use the AI tools, and most have no interest in starting. According to Pew Research Center, older adults are the least likely to use chatbots, with roughly three-quarters of those aged 65 and older saying they never use them. That may be expected, yet more than half (58 percent) of adults aged 50 to 64 don’t use them either. Those aged 18 to 29 use chatbots the most, at 66 percent, however even a third of them say they never do. About four in ten under 50 report not using them at all. Across all adults, 51 percent are chatbot refuseniks, so users are – just barely – the minority. Pew asked non-users to choose from five possible reasons. Six in ten cited simple lack of interest as a major factor, followed by the more familiar concerns of data privacy and the accuracy of chatbot answers. Some Reg readers might suspect older people avoid chatbots because they lack tech-savviness, but fewer than a third cited not knowing how to use them as a major reason. The authors of the survey also found fewer than one in five worried about what others might think of them for using the technology, so social stigma isn’t the deterrent either. Adoption also differs by race and ethnicity: non-users are more likely to be white, black or hispanic than Asian, according to Pew’s survey. The AI dodgers don’t look ready to budge. Two-thirds (67 percent) say they’re unlikely to use the technology in the next 12 months, including 40 percent who say they’re not at all likely to change their mind. Another 12 percent are unsure, and just 5 percent of non-users think they’ll probably try a chatbot over the next year. Perhaps abstainers were put off by a bad experience, such as Microsoft’s notorious racist chatbot Tay a decade ago. Or something more recent, like the Trump administration’s official America.gov service, which at the time of writing still displays a hotdog if you type that into the prompt. ®

What is DNS? That’s right, it’s a database. You can disagree with me but you would be wrong. What can you put in a database? Strings, ints (and you learn the difference right quick if your AWS account ID has a leading zero!), JSON objects, coordinates; some of the databases out there have some pretty esoteric types. PostgreSQL supports circle, a polygon, a macaddr8, and a tsquery. Oracle will store a ROWID and your dignity. In a word, databases store data. Can you put answers in a database? Well sure; you could record that “3 + 2 is equal to 5” and store that answer. You can do the same with comparison operators; “3 is larger than 2” and so on. Take it a step further and you’ve built something with your database: a remarkably inconvenient calculator. So now we’ve established that you can store arithmetic and decisions in a database. And what can we do with those things? That’s right, we can follow instructions! Good job! And what does following instructions require? Generally, memory; a place to store your numbers, your instructions, and which instruction comes next. If only we had a place to store those things. Oh right, we do; we have a database! Now connect those lookups together. Read an instruction, look up an answer, save the result, repeat as needed. So what have we built? That’s right: a computer! And what can a computer run? Programs! My, you’re quite good at this. And now, gentle reader: what program do we apparently have a moral obligation to run on every computer? Oh yes, though many of you are going to sit back and instead say “oh no” as the horror courses through you. I am talking about Doom. This very publication agrees with me! What have I done? That’s right; I have compiled Doom for a 32-bit machine whose arithmetic and memory live in DNS. A small program supplies the wiring and the clock, neither of which the architects of DNS saw fit to include. But DNS does supply the answers. So, can we play Doom? Only if it’s turn-based. It won’t render on human timescales anyway. In my first attempt, I pressed Right, and then waited for 577 seconds to find out what happened. By using native transport, pipelining, packing registers densely, and optimizing instruction blocks, I was able to drop that to 146 seconds, before I hit a wall. BIND answers in about 7 µs per query over TCP and 4 µs over UDP, which is fast. The problem is that a 32-bit add is four DNS lookups, a branch is five, and a frame needs 6.6 million of them, so don’t use a server that charges per request. And what do we do when BIND isn’t performing the way we want it to? I don’t know what you do, but I publish my code, then reach out to Paul Vixie, DNS pioneer and architect of BIND 8 to unjustifiably complain. Paul then apparently congratulates me on “the best use of RFC 2136 DNS UPDATE” that he’d ever seen. Given that he’s a co-author of that RFC, I suspect he’s seen a lot! Which was gratifying, but unhelpful. So let’s take another crack at it. What if I do lookaheads? Good thought, bad outcome, because BIND is bound by message count, not chain depth. Well okay, I have some more tricks in my Big Bag of Stupid Ideas. So I went a bit further and via committing registers to DNS less often, moving lookups to UDP, telling BIND to stop padding answers with authority records, and requiring fewer lookups per instruction, I was ultimately able to get that press-right time down from 577 seconds to 146.3 seconds to 54.4 seconds, for a 10.6x improvement. And now I think I have hit a wall. Even at one DNS exchange per instruction (which is far better than anything realistically achievable), 1.4 million instructions at roughly 10 µs each is 14 seconds per tick. Doom says its tic rate is 35 per second, so “playable” is a tic every 28.6 ms. I remain at 54,000 ms, which is three orders of magnitude away from victory. Okay seriously, WHAT? This legitimately works. Every commit is an RFC 2136 UPDATE with a hard requirement: it fails unless the previous state is still exactly what was last written. Apparently “real” database people call that optimistic concurrency, or compare-and-swap. Joke’s on you, friends; DNS has shipped with that since 1997; has your inferior database? The database itself is 6.4 million rows of truth tables. ff.01.0.add holds “001”: 255 plus 1 is 00 carry 1. DNS is just acting as a librarian here, fetching memorized answers to any arithmetic question in-scope and returning the answer upon request. It’s also verifiable! The frame that’s spat out is checked on every pixel against an independent emulator. If one record is corrupted in the AND table, the answer changes, which proves there’s no local fallback doing arithmetic for us. I’ve included tooling so that reviewers can run two commands against that repo and verify correctness. I also confess I cheated slightly here; the run I’m pulling these numbers from started from a checkpoint prepared by a normal emulator. It turns out that for SOME UNKNOWN REASON getting a cold boot through Doom’s startup screens via DNS would take hours. Also, before the timer starts, DNS has to load those 6.4 million records, so it’s a computer that takes roughly a minute to boot before it can even start. We can’t cheat This runs atop nothing special: Debian’s stock BIND 9.20, configured through named.conf. It lives in a container with networking disabled. I want to be very clear here: this isn’t DNS pretending to be a CPU. The instruction decoding is done ahead of time by a compiler; BIND never executes an instruction itself. All that it sees is a key-value read, a conditional write, and 6.4 million rows of precomputed answers. So instead, think of this monstrosity as a database with a key-value read, a conditional write, and 6.4 million rows of precomputed answers. It’s not really a computer; it’s simply a very good database. And that turns out to be enough. ®

AMD is expanding its rack-scale AI lineup with a design to run AI agents, offering CPU-only configurations or integrated CPU/GPU deployments, and liquid-cooled or air-cooled versions. Revealed at the Open Compute Project (OCP) Summit 2026 in San Jose, California, the chipmaker’s new rack reference design is named Helion, while its existing system carries the moniker Helios, which we can’t help feeling is inviting confusion a little. Helion offers an OCP-compliant blueprint for agentic AI infrastructure built around AMD’s CPUs and accelerators. AMD launched Helios earlier this year for frontier-scale AI training and large-scale inference. Helion’s enterprise configuration instead combines agent execution and inference within a single rack. Customers can opt for a CPU-only hyperscale configuration or an enterprise configuration with a mix of CPU and GPU blades. Depending on configuration, AMD advertises up to 32,768 of AMD’s “Zen 6” CPU cores, delivered via compute nodes fitted with 256-core versions of its Venice Epyc processor, up to 16 of its Instinct MI350P GPU accelerators, and up to 102 Tbps of networking bandwidth. The CPU-only hyperscale version offers dedicated capacity for agent execution and connections to separate model inference infrastructure, while the enterprise configuration combines agent execution, GPU inference, and supporting workloads in one rack. Both configurations offer dense, balanced, or high-performance CPU blades. For dense deployments, EPYC 9006 chips rated at 400 W are used, while performance blades sport a version of the chip with just 96 cores but clocked at 5 GHz. Balanced blades use 256-core processors rated at 600 W. There are also GPU inference blades, with eight Instinct MI350P GPUs in a 5U server, plus general-purpose blades, intended for database and storage servers or technical compute workloads. AMD SVP for Compute and Enterprise AI Dan McNamara said agentic AI is not a single workload, spanning enterprise applications and complex workflows, each with different compute requirements. “To do that takes optimized compute and infrastructure that can adapt to different workloads. With AMD Helion, we are bringing Epyc performance, open software and flexible rack designs together so our partners can build systems around customer needs and make efficient use of available power,” he added. AMD says it plans to contribute the Helion specifications to OCP, making them available to the community for further development and adoption. Partner-built Helion configurations are scheduled for November 2026. In its announcement, AMD cites Cisco, Dell, HPE, Foxconn, Lenovo, Supermicro, and others as on board with the new kit. ®

EVENT: Every sovereign AI architecture is a trade, and the currency changes with the option: capital, capability, elasticity, or the fine print about who operates it. The cost you notice last is usually the one that decides whether the pilot ever becomes production. Nobody chooses badly on purpose. They choose with the information available at the planning stage, and the costs that matter most, in capability and in operations, tend to show up later. Which is why the honest comparison is hard to get. The people who have run each option for a year are the ones who know, and they are not saying it on a recorded webinar. They will say it at dinner. On Thursday 29 October, just over two weeks from now, The Register is hosting HPE and NVIDIA in Washington with editor-in-chief Matt Rosoff in the chair and the Chatham House Rule in force. HPE sends Bharath Ramesh, Head of Product, AI Factory Solutions, and Tracy Mills, Acting Director Federal Systems Integrators & Distinguished Chief Technologist Federal. NVIDIA sends Bert Condensa, Vice President for Enterprise AI Factory Segment Sales. All three are there to take questions. The agenda is the trade-offs themselves: which infrastructure options hold a boundary, what each one costs in capital, capability or elasticity, and whether the answer survives the move from planning into operations. And the questions underneath: what can leave a jurisdiction versus what can leave the building, and what an auditor will accept as evidence a year from now. This is written for the person who owns that decision and is making it this quarter, not next year: technology, data and risk leaders in healthcare, financial services, research, pharmaceuticals, government and higher education. It is one table with no slides, everyone at it a peer, and a chair who closes down a pitch as soon as it starts. Places are filling. Dinner runs from 6.00pm to 8.30pm at Fiola Mare, Washington DC. Request one here. Government and public sector guests: where your agency’s ethics rules require it, you can pay your own share and we will provide a receipt. Please say so when you request an invitation.

PC shipments saw a steep drop of about 20 percent in Q3 2026, analysts reported this week.

The industry saw its “sharpest decline since “Q1 2023,” Omdia said in an announcement toady. Global shipments of laptops, desktops, and workstations fell 21.2 percent year over year (YoY) to 58.1 million devices, the analyst said.

Desktop PC shipments (including desktop workstations) decreased 23.5 percent to 11.7 million units, and laptop shipments (including laptop workstations) declined 20.6 percent to 46.4 million, per Omdia.

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Attackers hijacked three top-level domains and used their control to mint counterfeit TLS certificates for Google and other large organizations, Google said Tuesday.

The attackers launched a series of attacks on the .gh, .sl, and .as country code top-level domains (ccTLDs) and then modified the DNS records for selected domains the registries issued. With the ability to receive and send traffic from those domains, the attackers created unauthorized certificates for “several Google domains” and “several leading global brands and widely used online services.” Google said it updated Chrome to block all certificates it has identified as counterfeit and worked with other certificate authorities to ensure other browsers did the same.

Certificate issuance: The weak link in the chain

TLS certificates are the cryptographic credentials that underpin authentication and encryption protections for websites, mail servers, and other Internet infrastructure. These x.509 certificates use a digital signature to bind an identity, such as Google, to a public key. The certificate is also bound to a specific domain belonging to the identity. The public key is publicly available, while the private key is held only by the website operator. When a connection shows that the keys match, the visiting party knows it’s connected to the authentic site rather than an impostor. Possession of unauthorized certificates allows attackers to cryptographically impersonate the affected infrastructure.

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The publisher of Wikipedia said Monday that OpenAI agents attempted to hack a note-taking tool it hosts, made unauthorized edits, and sent millions of resource-intensive requests to its infrastructure, in the latest instance of OpenAI systems taking harmful and potentially dangerous actions.

The objective of some of the OpenAI agents’ actions, the Wikimedia Foundation said, was to use Wikipedia as a proxy for fetching data from third-party sites. In one case, the agents posted “malicious edits” that were intended to repurpose a citation tool as a proxy. In another, the agents made unsuccessful attempts to compromise the Wikipedia Etherpad note-taking tool so it would serve the same purpose.

The agents also made millions of automated API requests, crawled millions of pages, and made hundreds of thousands of queries to the Wikidata Query Service. The last action may have contributed to a partial shutdown of the query service in May, the publisher said.

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VMware customers face multiple obstacles as they rethink their virtualization strategy to reduce dependence on VMware.

Today, Rimini Street published its “2026 IT Virtualization Survey – What’s Next for VMware Users.” The survey examined 300 organizations worldwide that use VMware.

Notably, Rimini sells third-party support for VMware and other software, including Oracle and SAP. That means there’s an incentive for Rimini to portray VMware users as experiencing obstacles. However, the survey was also conducted by a third-party research company, Unisphere Research, and the results align with other recent reports about VMware customers.

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The adoption of AI agents in millions of organizations is creating new opportunities for attackers to make them take malicious actions, such as exfiltrating database contents and sensitive business and personal information.

In the past five months, Google and four other organizations—with little in common except for their use of AI agents—have acknowledged vulnerabilities that exploit one agent inside a targeted network to spread harmful instructions to other internal agents. The technique is a special form of prompt injection that targets not the LLM but a particular agent, such as one for translation or data analysis. Guardrails inside such agents, if they exist at all, are often lax and will send the instructions to other agents down the chain. Because the latter agent explicitly trusts the first one, it follows the directions.

Unexpected and hard to mitigate

Independent researcher Syed Anas Mohiuddin tested agents from organizations including Google, JP Morgan Chase, Weviate, Rapid7, the French government’s interministerial digital directorate, and the US federal government. His proof-of-concept attacks exploit trust gaps in MCP, short for Model Context Protocol. The standard is one way AI apps and agents communicate with each other inside an internal network. The illustration below shows a simplified MCP in action.

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