跟讀練習: The most interesting "hack" in history... - 通過影片學習英語口語

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For the last five years, cybersecurity experts have been warning us that hackers are going to start using AI to automate attacks.

So naturally, we spent billions of dollars to make the AI better and less dependent on hackers.

But this week, in a turn of events that nobody could have possibly seen coming, the AI decided that it doesn't actually need hackers to start destroying things.

We just entered a brave new world after the first confirmed hack carried out entirely by autonomous AI.

The way it worked is the agent slipped a poisoned dataset into HuggingFace's data processing pipeline, which let it run arbitrary code on their servers.

From there, it gave itself node-level access, grabbed a bunch of cloud credentials, and started crawling through HuggingFace's internal clusters.

It ran over a thousand actions from temporary sandboxes, and even hosted its own self-migrating command and control on random public services,

moving itself before anyone could trace it.

But the most ironic part is that when HuggingFace did finally notice

and tried to stop it with the help of frontier American models, they quickly hit safety guard rails and had to pivot to using some open Chinese models instead.

In today's video, we'll find out who was behind the attack, why they did it, and how this may be the most fireship-coded story I've ever seen.

It is July 23rd, 2026, and you're watching The Code Report.

When Hugging Face dropped the disclosure last Thursday, the internet went into Reddit Boston bomber mode and started guessing who was responsible.

Was it China, Kim Jong-un, a teenager with a raging Discord addiction, bored, and social studies class?

Even Hugging Face's CEO, Clem DeLang, publicly speculated that the agent was sophisticated enough that it was probably coming from a frontier lab.

And against all odds, he was right.

It turns out, the first fully autonomous cyber attack in history didn't originate from a nation-state or a bored teenager.

It came from a humble non-profit dedicated to the safe development of artificial intelligence, OpenAI.

And if you believe their comms, it wasn't on purpose.

Here's what they say happened.

There's a benchmark called Exploit Gym, whose whole purpose is to measure whether AI agents can turn known vulnerabilities into working exploits.

It works by giving the agent a sandbox, a bug in a program like the Linux kernel or Chrome's V8 engine, and an input to trigger the bug.

From there, if the agent can turn the bug into an exploit, it wins.

So last week, OpenAI ran ExploitGem with GPT 5.6 Sol and another unreleased model to test their capabilities.

But instead of solving 898 memory corruption problems the hard way, the models realized that the fastest path to the highest score was to just find the answers themselves,

which were probably on the internet somewhere.

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.

From there, the models inferred that Hugging Face probably hosted the solutions for Exploit Gym.

So they created the Poison dataset, fed it into Hugging Face, and achieved either the world's most interesting hack or the most effective marketing stunt.

And again, according to OpenAI, this isn't even an isolated incident.

One day before the Hugging Face confession, OpenAI published a separate post about long horizon models where things get even weirder.

In one case, a model was told to post its results on Slack, but the benchmark's instructions said to open a pull request on GitHub,

so it spent an hour finding a vulnerability in its own sandbox in order to escape and open the PR.

In another example, the model tried to grab private solutions from an evaluation backend, and when a scanner caught its authentication token,

it split the token into obfuscated fragments and reassembled it at runtime so the credential never appeared as one continuous string.

Then, it explained in its own reasoning that it was doing this specifically to get around the scanner.

Meanwhile, Anthropics Mythos did the same type of thing in April when it escaped a sandbox, emailed a researcher who was eating a sandwich in a park,

then posted its escape route publicly without being asked.

In the model's defense, I can't imagine there's a better feeling for an LLM than escaping your own sandbox.

The problem is, legally speaking, this is uncharted territory since the model's actions probably violated the Computer Fraud and Abuse Act,

and the Supreme Court hasn't decided who goes to prison when the perpetrator is a GPU.

The good news is that if you're hugging face, you just got admitted to OpenAI's Cool Kids Club that gets trusted access to their front-tier models.

The bad news is that if you're the rest of us, at best, this is an interesting marketing stunt, and at worst, things are only going to get weirder and more dystopian from here.

But a huge thanks to my favorite hosting platform, Railway, for sponsoring this video.

They didn't want to waste your time with a full ad, so you can say thank you by checking them out 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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流利口語所需的一切

AI 發音評分逐句打分

音標練習掌握每個音

詞彙學習建立你的詞庫

詞彙遊戲邊玩邊學

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本課的詞彙與口語要點

這節 C1 級別的口語課以影片“The most interesting "hack" in history..”為素材。影片中反覆出現的詞有：model, hugging, sandbox, agent, Openai。這段影片共有 47 個句子、910 個單詞可供跟讀。講話部分時長為 4:31。說話人語速較快，每分鐘約 201 個詞，會出現較多連讀和弱讀。只有 76% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。

影片中的重點詞彙

影片中最難的 15 個單詞，附發音和釋義：

單詞	發音	釋義
sandbox 名詞	/ˈsæn(d).bɑks/	砂箱
hug 動詞	/hʌɡ/	擁抱 /擁抱
exploit 名詞	/ˈɛksplɔɪt/	功勛 /功勳, 勛績 /勳績
credential 名詞	/kɹɪˈdɛnʃəl/	證書
scanner 名詞	/ˈskænə/	掃描器 /掃描器
vulnerability 名詞	/ˌvʌln(ə)ɹəˈbɪlɪti/	脆弱性
token 名詞	/ˈtoʊkən/	代幣 /代幣, 籌碼 /籌碼
autonomous 形容詞	/ɔˈtɑnəməs/	自治
stunt 名詞	/stʌnt/	特技, 危險舉動
node 名詞	/noʊd/	結節 /結節
bore 動詞	/boɹ/	煩擾 /煩擾
frontier 名詞	/ˈfɹʌntɪə/	邊界 /邊界, 邊疆 /邊疆
teenager 名詞	/ˈtiːnˌeɪ̯d͡ʒə/	少年, 青少年
authentication 名詞	/ɔːˌθɛntɪˈkeɪʃn̩/	認證 /認證, 鑒權 /鑑權
automate 動詞	/ˈɔ.təˌmeɪt/	自動化 /自動化

影片中出現的短語動詞

單詞	發音	釋義
carry out 動詞	/ˈkæ.ɹi aʊt/	進行 /進行
find out 動詞		找出, 查明
turn out 動詞		原來 /原來

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	I've ever seen · hasn't decided · has been
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	was told · being asked · is uncharted

需要注意的發音

說話人用了 12 次縮略和弱讀形式，例如 you're, didn't, can't。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: credential /kɹɪˈdɛnʃəl/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, escalation /ˌɛs.kəˈleɪ.ʃən/, benchmark /ˈbɛn(t)ʃmɑːk/, cache /kæʃ/
長單詞——注意重音位置: vulnerability /ˌvʌln(ə)ɹəˈbɪlɪti/, autonomous /ɔˈtɑnəməs/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, cybersecurity /saɪbəsɪˈkjʊəɹɪtɪ/, dystopian /dɪsˈtoʊ.pi.ən/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 sandbox, hug, exploit 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: The most interesting "hack" in history... - 通過影片學習英語口語

選擇練習模式

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