Shadowing Practice: The most interesting "hack" in history... - Learn English Speaking with Video
Creating lesson...
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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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Vocabulary and speaking notes for this lesson
This C1 speaking lesson is built on the video “The most interesting "hack" in history..”. The speaker keeps coming back to these words: model, hugging, sandbox, agent, Openai. This video has 47 sentences and 910 words to shadow. The speech runs for 4:31. The speaker talks fast, about 201 words per minute, so expect linked and reduced sounds. Only 76% of the words are among the 3,000 most common in English, so the vocabulary is demanding.
Key vocabulary in this video
The 15 most advanced words in the video, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| sandbox noun | /ˈsæn(d).bɑks/ | A children's play area consisting of a box filled with sand. |
| hug verb | /hʌɡ/ | To cling closely together. |
| exploit noun | /ˈɛksplɔɪt/ | A heroic or extraordinary deed. |
| hacker noun | /hækəɹ/ | Someone who hacks. |
| credential noun | /kɹɪˈdɛnʃəl/ | documentary or electronic evidence that a person has certain status or privileges |
| scanner noun | /ˈskænə/ | One who scans. |
| vulnerability noun | /ˌvʌln(ə)ɹəˈbɪlɪti/ | The state of being vulnerable; susceptibility to attack or injury, either physical or emotional; the state or condition of being weak or poorly defended. |
| token noun | /ˈtoʊkən/ | Something serving as an expression of something else. |
| autonomous adjective | /ɔˈtɑnəməs/ | Governing itself independently. |
| stunt noun | /stʌnt/ | A daring or dangerous feat, often involving the display of gymnastic skills. |
| node noun | /noʊd/ | A knot, knob, protuberance or swelling. |
| bore verb | /boɹ/ | To inspire boredom in somebody. |
| frontier noun | /ˈfɹʌntɪə/ | The part of a country which borders or faces another country or unsettled region. |
| teenager noun | /ˈtiːnˌeɪ̯d͡ʒə/ | A person between thirteen and nineteen years old (inclusive). |
| authentication noun | /ɔːˌθɛntɪˈkeɪʃn̩/ | Something which validates or confirms the authenticity of something; or the process of doing so. |
Phrasal verbs you will hear
| Word | Pronunciation | Meaning |
|---|---|---|
| carry out verb | /ˈkæ.ɹi aʊt/ | To hold while moving it out. |
| find out verb | To discover, as by asking or investigating. | |
| get around verb | To move to the other side of (something, such as an obstruction) by deviating from a direct course or following a curved path. | |
| run over verb | To exceed the allotted time. | |
| turn out verb | To end up; to result. |
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Passive voice be + past participle — the focus is on what happens, not who does it | was sophisticated · was told · being asked |
| Present perfect have/has + past participle — a past action that still matters now | I've ever seen · hasn't decided · has been |
Pronunciation to watch
The speaker uses 12 contractions and reduced forms, such as you're, didn't, can't. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: credential /kɹɪˈdɛnʃəl/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, escalation /ˌɛs.kəˈleɪ.ʃən/, benchmark /ˈbɛn(t)ʃmɑːk/, cache /kæʃ/
- Long words — get the stress right: vulnerability /ˌvʌln(ə)ɹəˈbɪlɪti/, autonomous /ɔˈtɑnəməs/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, cybersecurity /saɪbəsɪˈkjʊəɹɪtɪ/, dystopian /dɪsˈtoʊ.pi.ən/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like sandbox, hug, exploit.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.