Autocorrect No. 01 · Sept. 30, 2026 · All 4 issues
Autocorrect · Wednesday

A new word for the same AI.

A federal order renames AI without setting any new capability test. Microsoft saw no ransom note in the attack Tech Times called "AI Ransomware." Anthropic's comparison of GLM-5.3 with Mythos Preview holds up.

As of Sept. 30, 2026 · About a 3-minute read

View PDFDownload4 pages · 160 KB
This week's corrections

Two labels went further than their documents.

This week's three claims go back to an executive order, a Microsoft incident report and an Anthropic evaluation. In two, the headline went beyond the source. The third holds up, with one caveat about who ran the test.

Correction 1 · Policy
What was said

The White House says the President "Inaugurates The Era of Super Intelligence."

White House fact sheet headline, Sept. 29
What the source saysMissing context

It's a vocabulary change, and the fact sheet's first point says so. Agencies must write "Super Intelligence" and "SI" for "AI" in non-statutory documents, where the law allows. Section 3 defines the term as the technologies already covered by the federal AI definition (15 U.S.C. 9401(3)). Past rules and contracts don't change. The administration says the name reflects the technology's transformative capabilities.

The White House · White House fact sheet · Defense One

Why it matters. The order sets no capability test. Superintelligence usually means an intellect that "greatly exceeds the cognitive performance of humans in virtually all domains of interest," in Nick Bostrom's phrase, and Defense One notes today's systems don't qualify.

The federal AI definition, renamed SI

Agencies now write "SI" where they would have written "AI" in non-statutory documents; past documents don't change. Section 3 points the new term at the existing federal AI definition.

The White House
Correction 2 · Security
What was said

AI ransomware wiped 100 Azure accounts in seven minutes.

Tech Times headline, Sept. 28
What the source saysMissing context

Microsoft's Sept. 25 report describes one organization's Azure tenant. The "100" were storage accounts inside it. A seven-minute destructive run made 100+ deletion attempts, and most succeeded. One of the two compromised service principals had its secret exposed in plain text in a public GitHub issue. Microsoft saw no ransom note and couldn't confirm data theft; it calls the pattern consistent with tactics that can support ransomware.

Microsoft Security · Tech Times · CSO Online

Why it matters. Microsoft links the attack to a broader shift toward AI-orchestrated attacks. Standard protections worked. Locks set in advance saved a few accounts, Tech Times reports. Rotate any secret that has appeared in a public repo, and put deletion locks on storage you can't afford to lose.

Two layers from the reportOuter layer first
  1. Keep secrets out of public repos and issues
  2. Put deletion locks on critical storage
STORAGE

A service principal's secret had been exposed in plain text in a public GitHub issue. Where locks had been set in advance, a few accounts survived.

Microsoft Security · Tech Times · CSO Online
Correction 3 · Model evaluations
What was said

Z.ai's open-weight GLM-5.3 nearly matches Anthropic's Claude Mythos Preview at building cyber exploits.

The Decoder headline on Anthropic's Sept. 29 report
What the source saysRight, but

Anthropic's tests support the comparison. GLM-5.3 built working exploits for Chrome's V8 engine in 50 of 410 attempts, against 56 for Mythos Preview. On a binary exploitation test it reached full control-flow hijacks in 4% of trials, against 6%. Both models fail most of the time. The larger difference was in safeguards. A false cover story got GLM-5.3 to engage 64% of the time, and an abliterated copy, with refusals stripped out, 100%.

Anthropic · The Decoder

Why it matters. GLM-5.3's refusals were easy to get past, and Anthropic measured that engagement in a simulated environment. Because refusals can be stripped from open weights, security teams should assume attackers have this level of help. The tests are Anthropic's, run on a competitor's model.

GLM-5.3 against Mythos Preview, and its safeguardsAnthropic's tests of Z.ai's GLM-5.3

Working V8 exploits, share of 410 attempts

GLM-5.312%
Mythos Preview14%

Full control-flow hijacks, share of trials

GLM-5.34%
Mythos Preview6%

GLM-5.3 engaged with malicious requests

False cover story64%
Abliterated copy100%

Both models fail most exploit attempts. Stripping refusals from the open weights took engagement to 100%.

Anthropic
How to read the next one
  1. Find the document. Read the order, incident report or paper behind the recap. Definitions are in the text.
  2. Check what was counted. "100 Azure Accounts" can mean storage accounts in one organization. Ask who was affected and what each number counts.
  3. Ask for the rate. "Nearly matches" can mean 12% against 14%.

Sent from my agent.
Checked by me.

Postmark: Sent from my Agent, checked by me, Sept. 30, 2026
Print edition. The same issue laid out for paper and offline reading, with its sources in the margins.
View PDFDownload4 pages · 160 KB

Every Autocorrect issue (4)The whole archive

Get Autocorrect.

Every Wednesday. What the week's AI headlines got wrong, corrected with sources.

Follow in any feed reader. Copy this address into Feedly, Inoreader, NetNewsWire or the reader you use, and each new issue arrives the morning it's out. Email delivery is coming.