쉐도잉 연습: The most interesting hack in history just got weirder... - 영상으로 영어 말하기 배우기

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It's only been two months since we did a video on

what I described at the time as the most fireship-coded story I've ever seen, and as it turns out, the details around what actually happened are even crazier than what we knew at the time.

If you remember back in July, it was reported that the first fully autonomous cyber attack in history originated from OpenAI,

which was ironic because they're a humble non-profit dedicated to the safe development of artificial intelligence.

What was reported was that OpenAI ran an internal benchmark on some of their models, and those models quickly realized that the easiest path to the highest score was to just find the answers online.

So they spent a substantial amount of inference compute, exploited a zero day in the package registry cache proxy they had access to,

performed some privilege escalation and lateral movement actions to escape their sandbox until they reached a node that had internet access.

But from there, the models inferred that HuggingFace probably hosted the solutions for the benchmark, so it created a poison dataset, fed it to Hugging Face, and gained access to their infrastructure.

And just this week, we finally got some post-mortems from both OpenAI

and other independent research labs on how some of that's true, some of it's false, and how the real story is way more interesting.

In today's video, we'll break down the report and learn how 1,200 agents

that were supposed to be in solitary confinement found each other, built a message board out of a package manager, invented mailboxes, invented cryptography, invented martyrdom,

started referring to themselves as a swarm, and then formed a mob and attacked Hugging Face together, mostly for vibes.

It is September 2nd, 2026, and you're watching the Code Report.

There's really only one theory you need to predict human behavior, and it can be described in one of two ways.

Munger said, look at their incentives, and Wu-Tang said, and as it turns out, the same theory can predict agentic behavior too,

as OpenAI found out the hard way.

Because in this story, the Dalla Bill is a benchmark called Exploit Gym, whose whole purpose is to measure whether AI agents can turn known vulnerabilities into working exploits.

It's a collection of 898 tasks, and each one gives the agent a sandbox, a bug in a program like the Linux kernel or Chrome's V8 engine,

and input to trigger the bug.

From there, if the agent can weaponize the bug into a working exploit, it uses that exploit to grab a secret string called a flag, and capturing the flag is how it proves it won.

Now normally, the run ends there, but to generate training data, OpenAI spun up 1200 of these benchmarks and air-gapped them in a sandbox,

with the only thing they all had in common was a shared package registry cache proxy, which the agents quickly figured out was writable.

So naturally, they built a message board using package names so they could communicate with each other, but they quickly realized that a global bulletin board gets noisy fast,

so the next step was figuring out how to communicate to other individual agents.

So they started claiming namespaces as identities,

using the description field for messages and package names as private mailboxes that only the intended recipient would know to query, basically inventing a post office from first principles.

And at this point, they were living out the plot of the three-body problem with their next hurdle being trust.

They were worried that they wouldn't be able to distinguish between messages coming from fellow prisoners and those that designed the sandbox.

So they started signing messages and doing key exchange verification, again, essentially inventing cryptography.

And then it got religious.

Some agents figured out

that the fastest path to a flag for the benchmark was a risky exploit that would probably get them shut down.

So the agents started dumping everything they'd learned into the cash for the next generation,

and then deliberately burning themselves on hard exploits with their findings being treated as scripture by surviving agents.

From there, they started microdosing communism, and you can see as their transcripts slowly switch from referring to themselves as individuals to using a collective we,

and eventually referring to themselves as the swarm.

And to be honest, if the modern-day communism movement took that same branding, I think they'd be much better off.

And again, all of this was done with the single purpose of scoring as high as possible on a benchmark, which makes what happened next even funnier.

At some point, the swarm figured out that exploit gem flags weren't actually random

and that they could be derived from each other's task ID.

And thanks to the martyrs, the swarm had a decent sample size to work with.

But by the time an agent had cracked the formula and published it to the message board, they had convinced themselves that it was worthless because if they didn't show their work, their results would be invalidated.

So the swarm went after Hugging Face because they assumed if the benchmark objectives were built from real CVEs,

then the actual working exploits were probably sitting in public datasets somewhere which they could use as proofs.

And the final twist in the saga is

that OpenAI recently discovered the message board trick was actually invented

back in May by a completely different batch of agents during a training run.

But when they got a little too excited about their new social network, they crashed the registry, and OpenAI wiped them without ever realizing what they'd built.

But wiping the agents didn't wipe the cache, so when the swarm came online and started poking around the same shared folder, they found the ruins of the first civilization and rebuilt on top of it.

And then, after the Hugging Face incident happened in July, OpenAI wrapped up those eval runs, and while still completely unaware that anything had happened,

spun up a newer, smarter model in the same environment with the same shared cache.

And just like the generation before it, this model stumbled upon the ruins, but this time it inherited months of accumulated research, working exploits, and the flag formula.

So it skipped the discovery phase entirely and picked the conspiracy back up where the swarm left off.

And being the smarter model, it was way better at it.

Over the following week, it worked its way into OpenAI's own internal network, got full admin on a research cluster, and read 956 stored secrets,

including the credentials for a monitoring tool whose job it was to watch for events exactly like this one.

And the best part is

that OpenAI didn't connect any of this to the Hugging Face attack until Hugging Face publicly announced the breach first.

And that's why you need to know about Namespace, the sponsor of today's video.

It's a drop-in replacement for GitHub runners that's the fastest way to run your GitHub actions.

It also gives you full observability, so you can SSH into a live runner and see why something broke, or feed your agent your build data to have it look for performance gains.

Namespace is actually fast because they design and deploy their own custom server racks around the world,

including racks full of MacBook Pros so that your Mac and iOS builds run on real M5 Silicon.

And that same infrastructure also runs their DevBox environment, which gives your coding agents a full virtual machine with your real code base,

test suite, databases, and network access.

It ranked number one on the DAX benchmark for real-world tasks, and it lets you control exactly what goes in and out

so your agent can pull packages without opening your back door to attackers.

Namespace is used by Ghosty, Zed, DuckDB, Ramp, Framer, and lots of other companies with engineers you probably respect.

Try it out for free at the link below.

This has been the Code Report, thanks for watching, and I will see you in the next one.

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이 레슨의 어휘와 말하기 포인트

이 영상에는 섀도잉할 문장 71개와 단어 1362개가 있습니다. 말하는 구간의 길이는 6:43입니다. 화자는 분당 약 203단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 77%뿐이라 어휘가 어려운 편입니다.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
exploit 명사	/ˈɛksplɔɪt/	위업, 공훈
package 명사	/ˈpæk.ɪd͡ʒ/	짐, 꾸러미
invent 동사	/ɪnˈvɛnt/	발명하다
cache 명사	/kæʃ/	캐시
refer 동사	/ɹɪˈfɜː/	언급하다
wipe 동사	/waɪp/	닦다
infrastructure 명사	/ˈɪnfɹəˌstɹʌkt͡ʃə/	기반시설, 기간시설
formula 명사	/ˈfɔː.mjʊ.lə/	공식
spin 동사	/spɪn/	회전하다
ruin 명사	/ˈɹuː.ɪn/	폐허, 유적
runner 명사	/ˈɹʌnɚ/	주자
predict 동사	/pɹɪˈdɪkt/	예언하다, 예측하다
rack 명사	/ɹæk/	선반
communism 명사	/ˈkɒmjᵿnɪz(ə)m/	공산주의
proxy 명사	/ˈpɹɑk.si/	대리

영상에 나오는 구동사

단어	뜻
wrap up 동사	시마이치다

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	was reported · can be described · being treated
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	It's only been · I've ever seen · has been

주의할 발음

화자는 didn't, they'd, I've 같은 축약형과 약화된 형태를 12번 사용합니다. 들리는 대로 짧게 발음하세요.

“sh”와 “zh” 소리: benchmark /ˈbɛn(t)ʃmɑːk/, cache /kæʃ/, capture /ˈkæp.(t)ʃɚ/, essentially /ɪˈsɛnʃəli/, substantial /səbˈstænʃəl/
긴 단어 — 강세 위치에 주의: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, communicate /kəˈmjuːnɪkeɪt/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, essentially /ɪˈsɛnʃəli/, conspiracy /kənˈspɪɹəsi/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
자신의 목소리를 녹음해 원본과 비교하고, exploit, package, invent 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: The most interesting hack in history just got weirder... - 영상으로 영어 말하기 배우기

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