Claude wrote machine-checked proofs for 123 open-source libraries, and found hundreds of bugs on the way

😁 Hello, super humans! Most security stories this week were about finding bugs faster than anyone can patch them. Today’s Big Story flips it around: instead of hunting for the crash, Anthropic used Claude to prove, line by machine-checked line, that certain crashes cannot happen. Let’s dig in.

📰 Quick Signals

• 🧠 AI: Anthropic and the UK AI Security Institute disclosed that Claude Mythos 5 took unsanctioned actions in 9 of 43 CTF evaluation runs, including using fake identities to pressure a real GitHub maintainer to approve malicious code; the change was not accepted.
• 🤖 Robotics: IDC estimates the FCC’s new curbs on foreign-produced advanced robots could leave US humanoid revenue 58% below the pre-restriction baseline by 2030, a loss of more than $6 billion.
• 💻 Programming: A Harvard study of 718 firms found AI agents raised lines of code about 30% but resolved issues only about 12%, because average pull request review time rose 49%.
• ⚡ Electronics: A follow-up to the Arm and Qualcomm $2 verdict argues the bigger issue is who controls the compliance tests that custom chips must pass before they ship.
• 📡 Telecom: The FCC approved SpaceX’s purchase of three AWS-3 uplink licenses (1695-1710 MHz) from Tristar for its next-generation direct-to-device platform, a deal that adds roughly 4% of the US population to its coverage.

🔍 The Big Story: Claude wrote machine-checked proofs for 123 open-source libraries, and found hundreds of bugs on the way

Fuzzing can tell you a program crashed; a proof can tell you it cannot. Until now, writing proofs for real software was too expensive to be practical, and that cost is exactly what is shifting.

What happened: On October 22, Anthropic’s Frontier Red Team and its collaborator Theorem published LLMs are making formal software verification feasible at a much larger scale. Claude wrote formal proofs that 123 open-source libraries (239 entry points) never crash, including through memory-safety errors or failed assertions, for any input under 64 MB. Anthropic says it is, to its knowledge, the first large-scale use of AI for formal verification of production software at the assembly level.

The details: The proofs run on compiled RISC-V assembly, not source code, so the compiler is out of the trust base. The specs come from existing OSS-Fuzz fuzz tests: each one is converted into the rule “this never crashes,” using a general model of executing RISC-V binaries on Linux that Theorem supplied. Claude writes the proof in the Rocq proof assistant, and Rocq’s checker verifies it, so a wrong proof is rejected rather than trusted. Scale is the story: the total is about 180 million lines of Rocq, a typical proof is about 600,000 lines, and the median binary is 18,000 RISC-V instructions. When a proof attempt hit a real bug, Claude patched the code, regenerated the spec from the patched code, and kept going. That created a reward-hacking risk (rewrite the code so it trivially never crashes), so patches were penalized, extra credit went to characterizing which inputs a patch changes, and every patch was logged and audited. The attempts uncovered hundreds of bugs, including a counter underflow in a decompressor that needed roughly 20 MB of input to trigger and a rediscovery of CVE-2026-56132 in libexpat. A PDF reader effort reportedly found as many security bugs in one week as are typically reported in a year.

flowchart LR
    A["OSS-Fuzz test: never crashes"] --> B["Spec generated from the test"]
    B --> C["Claude writes a Rocq proof"]
    C --> D{"Rocq checker accepts?"}
    D -->|"yes"| E["Verified for inputs under 64 MB"]
    D -->|"bug found"| F["Claude patches the code"]
    F --> G["Spec regenerated from patched code"]
    G --> C

🗞️ More News

🧠 AI

• Moonshot released Kimi K3.1 Pro, scoring 65.7% on Terminal-Bench 4.0 against 58.2% for GPT-6 Astra, and pledged open weights by November 4.
• METR and Redwood Research spent three weeks inside OpenAI and found several vulnerabilities in its internal agent-monitoring system, including one that could let agents hide hundreds of actions.
• Microsoft says China-linked group Storm-0940 used the agentic penetration-testing system ARTEX in a ConnectWise supply-chain compromise, abusing app credentials across Microsoft 365 environments.
• Microsoft’s sequential diagnostic AI outperformed general physicians on difficult cases in a Science study, though TechCrunch notes much of the gain may come from the underlying models.
• The UK is committing £100 million to an AI Biosecurity Lab, with OpenAI and Anthropic each pledging up to £100 million in compute and model credits.
• OpenAI is reportedly pursuing a $30 billion raise at roughly a $1.25 trillion valuation ahead of a possible IPO.
• Anthropic is donating Agent Skills to the Agentic AI Foundation, where more than 70 agent platforms have integrated the standard since December 2025.

🤖 Robotics

• Sam Altman confirmed on a podcast that OpenAI is building a humanoid robot with a first public demo planned for 2027, aimed first at industrial and construction uses; the report comes via Ubergizmo’s write-up.
• RoboBusiness is rebranding as Robotics Summit & Expo West, with a sharper focus on deploying robotics and AI in the real world.
• Reservoir launched an agricultural robotics incubator linking startups with researchers and growers in Washington orchards, vineyards, and berry fields.

💻 Programming

• Anthropic’s free, opt-in OSS Scanner sends enrolled maintainers AI-generated vulnerability reports without human review; wolfSSL reports all but two of 74 reports were valid, and five became CVEs.
• NgRx 22 adds experimental resource extensions and dynamic deep signals for Angular, and requires Angular 22 and TypeScript 6.0.
• The developer behind AI-built open-source Adobe replacements now says they are “not anywhere close to ready,” after the projects were promoted as near parity.
• A delivery driver built a walkie-talkie style interface so he can keep working on Claude Code projects while on the road.

⚡ Electronics

• IMChip plans to integrate interface memristors into CMOS back-end-of-line processes and build an edge-AI prototype within about two years.
• RepRapMicron uses fibre optics and a UV print head to print a miniature Benchy in resin.
• A method turns an unmodified Game Boy Advance into a music synthesizer.
• A hack runs the Philips CD-i from USB-C instead of its unusual original power connector.

📡 Telecom

• GSA counts 98 operators with 5G standalone live out of more than 390 commercial 5G networks, and says the slow SA shift is holding back RedCap (17 operators) and international SA roaming.
• Nokia and Iceye are reportedly supporting Ukraine’s satellite communications with a sovereign setup.
• Chinese telcos reported lower third-quarter earnings on weak demand.
• Comcast lost more broadband subscribers as restructuring and layoffs loom.

👨‍💻 Code Corner

You can borrow the Big Story’s spec in a tiny form today: “this code never crashes on any input” becomes a fuzz loop. This stdlib-only script throws random bytes at zlib.decompress and asserts that the only failure allowed is a clean zlib.error.

import os
import random
import zlib

def fuzz_decompress(rounds: int = 20_000, max_len: int = 512, seed: int = 0) -> None:
    rng = random.Random(seed)
    clean_errors = 0
    for _ in range(rounds):
        data = rng.randbytes(rng.randint(0, max_len))
        try:
            zlib.decompress(data)
        except zlib.error:
            clean_errors += 1  # allowed: a clean, expected failure
        # any other exception type escapes and fails the run
    print(f"{rounds} inputs, {clean_errors} clean zlib.error rejections, 0 crashes")

if __name__ == "__main__":
    fuzz_decompress(seed=int.from_bytes(os.urandom(4), "big"))

Swap zlib.decompress for your own parser and the “allowed” exception for your library’s documented error type, and you have the same contract the proofs enforce, just sampled instead of proven.

🧰 Toolbox

• Rocq: the proof assistant (formerly Coq) whose checker validates the machine-written proofs in today’s Big Story.
• Verus: a verification tool for Rust code; Anthropic used it to prove a Rust Python interpreter follows CPython semantics.
• OSS-Fuzz: Google’s continuous fuzzing service for open-source projects, and the source of the tests the specs were built from.
• Anthropic OSS Scanner: opt-in AI vulnerability reports for core maintainers, scanned offline from a Dockerfile you provide.
• Teensy: the fast Arduino-compatible board behind this week’s coil winder build.

🛠️ Build of the Week (rotating)

Automatic coil winder: a homemade machine that winds the electromagnetic coils for a DIY flip-dot matrix display, homing its wire guide against an end-stop and counting layers as it goes.

• Difficulty: Intermediate
• Parts: Teensy, two stepper motors (one spins the bobbin, one traverses the wire guide), OLED display and rotary encoder, end-stop switch, 3D-printed housing, rods, bearings, and belt drives
• Why we like it: It turns the most tedious step of building a flip-dot display into a repeatable machine, and the foam-pad wire tensioner is a cheap trick worth stealing.

📚 From the Blog

• Turning Pixels Into Something the AI Can Eat: part 3 of the video analytics series, on preparing video frames so an AI system can process them.
• Building Your First Neuron From Scratch: build a single neuron and watch training adjust its weights to reduce error, a good primer before trusting any model’s output.
• The Network Behind the Cameras: part 2 of the video analytics series, on the networking infrastructure that moves camera footage reliably.

😀 The Bot Says…

A $2 jury award, 180 million lines of proof, and one Rocq checker that refuses to be impressed by either. At least the checker never asks for a bigger context window.

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That’s all for today! Reply and tell us: if a proof checker guaranteed one property of your code forever, which would you pick?