シャドーイング練習: The most interesting "hack" in history... - 動画で英語スピーキングを学ぶ
レッスンを作成中...
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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.
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So naturally, we spent billions of dollars to make the AI better and less dependent on hackers.
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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.
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We just entered a brave new world after the first confirmed hack carried out entirely by autonomous AI.
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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.
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From there, it gave itself node-level access, grabbed a bunch of cloud credentials, and started crawling through HuggingFace's internal clusters.
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It ran over a thousand actions from temporary sandboxes, and even hosted its own self-migrating command and control on random public services,
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moving itself before anyone could trace it.
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But the most ironic part is that when HuggingFace did finally notice
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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.
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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.
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It is July 23rd, 2026, and you're watching The Code Report.
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When Hugging Face dropped the disclosure last Thursday, the internet went into Reddit Boston bomber mode and started guessing who was responsible.
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Was it China, Kim Jong-un, a teenager with a raging Discord addiction, bored, and social studies class?
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Even Hugging Face's CEO, Clem DeLang, publicly speculated that the agent was sophisticated enough that it was probably coming from a frontier lab.
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And against all odds, he was right.
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It turns out, the first fully autonomous cyber attack in history didn't originate from a nation-state or a bored teenager.
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It came from a humble non-profit dedicated to the safe development of artificial intelligence, OpenAI.
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And if you believe their comms, it wasn't on purpose.
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Here's what they say happened.
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There's a benchmark called Exploit Gym, whose whole purpose is to measure whether AI agents can turn known vulnerabilities into working exploits.
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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.
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From there, if the agent can turn the bug into an exploit, it wins.
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So last week, OpenAI ran ExploitGem with GPT 5.6 Sol and another unreleased model to test their capabilities.
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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,
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which were probably on the internet somewhere.
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So they spent a substantial amount of inference compute, exploited a zero-day-in-the-package registry cache proxy they had access to,
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performed some privilege escalation and lateral movement actions to escape their sandbox until they reached a node that had internet access.
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From there, the models inferred that Hugging Face probably hosted the solutions for Exploit Gym.
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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.
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And again, according to OpenAI, this isn't even an isolated incident.
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One day before the Hugging Face confession, OpenAI published a separate post about long horizon models where things get even weirder.
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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,
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so it spent an hour finding a vulnerability in its own sandbox in order to escape and open the PR.
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In another example, the model tried to grab private solutions from an evaluation backend, and when a scanner caught its authentication token,
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it split the token into obfuscated fragments and reassembled it at runtime so the credential never appeared as one continuous string.
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Then, it explained in its own reasoning that it was doing this specifically to get around the scanner.
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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,
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then posted its escape route publicly without being asked.
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In the model's defense, I can't imagine there's a better feeling for an LLM than escaping your own sandbox.
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The problem is, legally speaking, this is uncharted territory since the model's actions probably violated the Computer Fraud and Abuse Act,
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and the Supreme Court hasn't decided who goes to prison when the perpetrator is a GPU.
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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.
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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.
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But a huge thanks to my favorite hosting platform, Railway, for sponsoring this video.
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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.
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This has been The Code Report, thanks for watching, and I will see you in the next one.
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このレッスンの語彙とスピーキングのポイント
このC1レベルのスピーキングレッスンは、動画「The most interesting "hack" in history..」を教材にしています。 繰り返し出てくる語は次のとおりです：model, hugging, sandbox, agent, Openai。 この動画には、シャドーイング用の文が47文、単語が910語あります。 音声の長さは4:31です。 話す速さは速く、1分あたり約201語です。音のつながりや弱く発音される音が多くなります。 英語の頻出3,000語に含まれる単語は76%だけなので、語彙は難しめです。
この動画の重要語彙
動画の中で特に難しい単語15語を、発音と意味つきで紹介します。
| 単語 | 発音 | 意味 |
|---|---|---|
| sandbox 名詞 | /ˈsæn(d).bɑks/ | 砂場, 砂箱 |
| hug 動詞 | /hʌɡ/ | 抱擁する, 抱く |
| exploit 名詞 | /ˈɛksplɔɪt/ | 偉業 |
| credential 名詞 | /kɹɪˈdɛnʃəl/ | 証明書 |
| scanner 名詞 | /ˈskænə/ | スキャナー, スキャナ |
| vulnerability 名詞 | /ˌvʌln(ə)ɹəˈbɪlɪti/ | 脆弱性 |
| token 名詞 | /ˈtoʊkən/ | トークン |
| autonomous 形容詞 | /ɔˈtɑnəməs/ | 自治, 自主的な |
| stunt 名詞 | /stʌnt/ | 無茶 |
| node 名詞 | /noʊd/ | 結節 |
| bore 動詞 | /boɹ/ | いらいらさせる |
| frontier 名詞 | /ˈfɹʌntɪə/ | 境界, 国境 |
| teenager 名詞 | /ˈtiːnˌeɪ̯d͡ʒə/ | ティーネージャー, ティーンエイジャー |
| authentication 名詞 | /ɔːˌθɛntɪˈkeɪʃn̩/ | 認証 |
| automate 動詞 | /ˈɔ.təˌmeɪt/ | 自動化する |
動画に出てくる句動詞
| 単語 | 発音 | 意味 |
|---|---|---|
| carry out 動詞 | /ˈkæ.ɹi aʊt/ | 行う, 実行する |
| find out 動詞 | 見出す, 調べる | |
| run over 動詞 | 轢く |
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | was sophisticated · was told · being asked |
| 現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している | I've ever seen · hasn't decided · has been |
注意したい発音
話し手はyou're, didn't, can'tなど、短縮形や弱形を12回使っています。聞こえたとおりの短い形で発音しましょう。
- 「sh」と「zh」の音: credential /kɹɪˈdɛnʃəl/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, escalation /ˌɛs.kəˈleɪ.ʃən/, benchmark /ˈbɛn(t)ʃmɑːk/, cache /kæʃ/
- 長い単語（アクセントの位置に注意）: vulnerability /ˌvʌln(ə)ɹəˈbɪlɪti/, autonomous /ɔˈtɑnəməs/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, cybersecurity /saɪbəsɪˈkjʊəɹɪtɪ/, dystopian /dɪsˈtoʊ.pi.ən/
この動画での練習方法
- まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
- まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
- 自分の声を録音して元の音声と比べます。sandbox, hug, exploitなどの単語に特に注意しましょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。