تدريب Shadowing: The most interesting "hack" in history... - تعلم التحدث بالإنجليزية عبر الفيديو
جارٍ إنشاء الدرس...
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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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المفردات وملاحظات التحدث لهذا الدرس
هذا الدرس في المحادثة بمستوى C1 مبني على الفيديو “The most interesting "hack" in history..”. أكثر الكلمات تكرارًا في الفيديو: model, hugging, sandbox, agent, Openai. يحتوي هذا الفيديو على 47 جملة و910 كلمة للتدرب على الترديد. مدة الكلام 4:31. يتكلم المتحدث بسرعة، نحو 201 كلمة في الدقيقة، لذا ستسمع كثيرًا من وصل الكلمات واختزال الأصوات. 76% فقط من الكلمات ضمن أكثر 3,000 كلمة شيوعًا في الإنجليزية، فالمفردات صعبة نسبيًا.
أهم المفردات في هذا الفيديو
أصعب 15 كلمة في الفيديو، مع النطق والمعنى:
| الكلمة | النطق | المعنى |
|---|---|---|
| sandbox اسم | /ˈsæn(d).bɑks/ | صُنْدُوق رَمْل, صَنْدُوق رَمْل |
| hug فعل | /hʌɡ/ | عَانَقَ, اِحْتَضَنَ |
| exploit اسم | /ˈɛksplɔɪt/ | مَأْثُرَة |
| scanner اسم | /ˈskænə/ | مَاسِح, مَاسِح ضَوئِيّ |
| token اسم | /ˈtoʊkən/ | رَمْز |
| autonomous صفة | /ɔˈtɑnəməs/ | اِسْتِقْلَالِيّ |
| bore فعل | /boɹ/ | أَمَلَّ |
| frontier اسم | /ˈfɹʌntɪə/ | حَدّ, حُدُود |
| teenager اسم | /ˈtiːnˌeɪ̯d͡ʒə/ | مُرَاهِق |
| authentication اسم | /ɔːˌθɛntɪˈkeɪʃn̩/ | مُصَادَقَة, اِسْتِيثَاق |
| automate فعل | /ˈɔ.təˌmeɪt/ | أَتْمَتَ |
| escalation اسم | /ˌɛs.kəˈleɪ.ʃən/ | تَصْعِيد |
| obfuscate فعل | /ˈɑbfəskeɪt/ | يُعَتّم |
| dedicate فعل | /ˈdɛdɪˌkeɪt/ | كَرَّسَ, قَدَّمَ |
| fragment اسم | /ˈfɹæɡmənt/ | قِطْعَة, شَقَفَة |
الأفعال المركبة التي ستسمعها
| الكلمة | النطق | المعنى |
|---|---|---|
| carry out فعل | /ˈkæ.ɹi aʊt/ | شَغَّلَ, نَفَّذَ |
القواعد في هذا الفيديو
أكثر التراكيب التي يستخدمها المتحدث، مع العبارات نفسها من الفيديو:
| التركيب | في الفيديو |
|---|---|
| المبني للمجهول be + اسم المفعول — التركيز على ما يحدث لا على من يفعله | was sophisticated · was told · being asked |
| المضارع التام have/has + اسم المفعول — حدث ماضٍ ما زال له أثر الآن | I've ever seen · hasn't decided · has been |
نطق يحتاج إلى انتباه
يستخدم المتحدث 12 من الصيغ المختصرة، مثل you're, didn't, can't. انطقها بصيغتها القصيرة كما تسمعها.
- أصوات “sh” و“zh”: credential /kɹɪˈdɛnʃəl/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, escalation /ˌɛs.kəˈleɪ.ʃən/, benchmark /ˈbɛn(t)ʃmɑːk/, cache /kæʃ/
- الكلمات الطويلة — انتبه لموضع النبر: vulnerability /ˌvʌln(ə)ɹəˈbɪlɪti/, autonomous /ɔˈtɑnəməs/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, cybersecurity /saɪbəsɪˈkjʊəɹɪtɪ/, dystopian /dɪsˈtoʊ.pi.ən/
كيف تتدرب بهذا الفيديو
- استمع إلى الفيديو كاملًا مرة واحدة دون أن تتكلم، ودوّن الكلمات التي لا تعرفها.
- ابدأ بسرعة 0.75×، وردّد جملة جملة، ثم عُد إلى السرعة العادية عندما يصبح الأمر سهلًا.
- سجّل صوتك وقارنه بالأصل، مع الانتباه إلى كلمات مثل sandbox, hug, exploit.
ما هي تقنية التظليل الصوتي؟
التظليل الصوتي (Shadowing) تقنية تعلم لغة مدعومة علمياً، طُورت أصلاً لتدريب المترجمين الفوريين المحترفين. الطريقة بسيطة لكنها قوية: تستمع لصوت إنجليزي أصلي وتكرره فوراً بصوت عالٍ — كظل يتبع المتحدث بتأخير 1-2 ثانية. تُظهر الأبحاث تحسناً كبيراً في دقة النطق والتنغيم والإيقاع وربط الأصوات والاستماع والطلاقة.