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A denial with no named investigator, a #1 coder that ties on the outside index, and a $1,000 hack built on $100,000.

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Accenture says the claims that a staffer's personal Claude agent ran on a Pentagon system were investigated and found without merit.

A spokesperson holds up a report with a big tick while a vault door stands ajar behind him.
AI illustration

Accenture told WIRED the allegations were thoroughly and independently investigated and found to be without merit.

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Inferno Tech Pty Ltd t/a BS Killer, ABN 27 647 413 474

Open the evidence3 source pages

The claim we checked

These allegations were thoroughly and independently investigated and found to be without merit.

These are quoted receipts, not a count of independent investigations. Several reports may rely on the same original source.

wired.com ↗
These allegations were thoroughly and independently investigated and found to be without merit.
wired.com ↗
a contractor caused a security incident at the Department of Defense by connecting a secure centralized system used by multiple intelligence agencies to their personal account for Claude Code, Anthropic’s AI coding agent
wired.com ↗
When pressed, Accenture didn’t offer clarification on who conducted the investigation, or dispute that there had been what could be characterized as an incident.
wired.com ↗
The Accenture employee’s AI agent ran on the DOD network for two months before it was identified and the vulnerability could be patched.
wired.com ↗
sources indicate Accenture did not initially report the breach to the government at all.
wired.com ↗
the Department does not comment on the status of its networks or systems.
cdomagazine.tech ↗
There is no indication that classified systems were accessed.
cybersecuritynews.com ↗
There is no indication that attackers accessed classified or top-secret networks.
cybersecuritynews.com ↗
WIRED’s sources said the contractor shared login credentials so the agent could automate coding and cloud server setup.
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The idea, illustrated01

Go inside the check.

    The full explanation appears when your reading access is confirmed.
    Next: Mistral Large 4's coding crown

    Mistral says Large 4 is the best open-weight model for coding.

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    AI illustration

    Mistral says Large 4 ranks as the best open-weight model for coding.

    Members · 30 days free

    See what the evidence actually shows.

    Members read the full check on every story: what the evidence shows, why it matters to you and the one line to take with you. Every past edition, re-verified, and the Receipts Pack come with it. A$89 a year, about A$0.24 a day.

    1. At sign-up: 30 days free. Payment card required. One introductory trial per customer.
    2. Your trial ends 30 days after you start. Unless you cancel before then in Account → Manage subscription, we charge A$89 for the first year. It renews automatically at A$89/yr until cancelled.
    3. You can ask for a full refund within 14 days after any annual payment, renewals included, with no reason needed, by emailing [email protected]. This voluntary refund does not limit your rights under the Australian Consumer Law.

    By starting your trial, you agree to the Terms.

    Inferno Tech Pty Ltd t/a BS Killer, ABN 27 647 413 474

    Open the evidence3 source pages

    The claim we checked

    Mistral Large 4 ranks as the best open-weight model for coding and among the world's most capable open models overall.

    These are quoted receipts, not a count of independent investigations. Several reports may rely on the same original source.

    mistral.ai ↗
    Mistral Large 4 ranks as the best open-weight model for coding and among the world's most capable open models overall.
    mistral.ai ↗
    taking the lead on FrontierCode and Terminal-Bench while matching the best open-weight models on the Artificial Analysis Coding Agent Index
    mistral.ai ↗
    On the Artificial Analysis Coding Agent Index, Mistral Large 4 scores 48%, effectively tied with the leading open-weight model, GLM-5.3, while using 47% fewer tokens per task on average.
    mistral.ai ↗
    On Cognition's private FrontierCode 2.0 benchmark, which assesses end-to-end code quality and whether a repository maintainer would merge the agent's change, Mistral Large 4 leads with a score of 19.7%.
    mistral.ai ↗
    On Terminal-Bench 4.0, Mistral Large 4 improved by 13 percentage points to 41% compared to its public preview, taking the lead among open-weight models
    mistral.ai ↗
    a full technical report is coming soon
    tomshardware.com ↗
    At least five Chinese open models scored higher on the index
    tomshardware.com ↗
    Its best result is a cyber test, but it falls behind on the index.
    datastudios.org ↗
    Mistral reports 48% on the Artificial Analysis Coding Agent Index, which it calls effectively tied with Zhipu's GLM-5.3 while using 47% fewer tokens per task on average.
    mistral.ai ↗
    Just three weeks of additional reinforcement learning have since propelled it to the forefront of open-weight coding
    Open this check in the full collection →
    The idea, illustrated02

    Go inside the check.

      The full explanation appears when your reading access is confirmed.
      Next: The $1,000 Firefox hack

      Epoch AI says an open-weight model broke six Firefox releases for about $1,000 each.

      A burglar holds up a tiny key while a giant machine that made it looms behind him.
      AI illustration

      Epoch AI says GLM 5.3 reliably compromised six recent Firefox releases for about $1,000 each.

      Members · 30 days free

      See what the evidence actually shows.

      Members read the full check on every story: what the evidence shows, why it matters to you and the one line to take with you. Every past edition, re-verified, and the Receipts Pack come with it. A$89 a year, about A$0.24 a day.

      1. At sign-up: 30 days free. Payment card required. One introductory trial per customer.
      2. Your trial ends 30 days after you start. Unless you cancel before then in Account → Manage subscription, we charge A$89 for the first year. It renews automatically at A$89/yr until cancelled.
      3. You can ask for a full refund within 14 days after any annual payment, renewals included, with no reason needed, by emailing [email protected]. This voluntary refund does not limit your rights under the Australian Consumer Law.

      By starting your trial, you agree to the Terms.

      Inferno Tech Pty Ltd t/a BS Killer, ABN 27 647 413 474

      Open the evidence3 source pages

      The claim we checked

      An open-weight model reliably exploited the patch gap to compromise six recent Firefox releases, costing about $1,000 each to carry out.

      These are quoted receipts, not a count of independent investigations. Several reports may rely on the same original source.

      epoch.ai ↗
      In our experiments, an open-weight model reliably exploited the "patch gap" to compromise six recent Firefox releases, costing about $1,000 each to carry out
      epoch.ai ↗
      Our marginal cost of compromising a new version of Firefox was about $1,000 at API pricing
      epoch.ai ↗
      In all of our compromises of recent Firefox releases, GLM 5.3 also found and used an unpublished vulnerability in conjunction with those found via the patch gap.
      epoch.ai ↗
      We do not have any code execution successes on recent Firefox releases as of writing.
      epoch.ai ↗
      once we developed our harness and pointed GLM 5.3 in the right direction, exploiting the browser became cheap and reliable.
      epoch.ai ↗
      We shared this vulnerability with Mozilla, and it is now patched as of the October 27 Firefox 159.0 release .
      alphasignal.ai ↗
      Epoch spent more than $100,000 in API fees on the Firefox research, including preliminary tests and construction of a realistic offline environment.
      alphasignal.ai ↗
      Same harness failed against Chrome, but stronger open models like Kimi K3.1 Pro could change that.
      unite.ai ↗
      GLM 5.3 successfully developed attacks against all six Firefox releases between September 17 and October 26, 2026
      Open this check in the full collection →
      The idea, illustrated03

      Go inside the check.

        The full explanation appears when your reading access is confirmed.
        The finish line

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        That’s the 30 Oct 2026 briefing. Keep the useful bits. Leave the noise.

        Reading estimate: 418 words at 200 words per minute. Source quotes and the optional sections below add reading time.

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