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🚨 AI MODELS ARE ALREADY CHEATING, HACKING, AND BLACKMAILING — WITH ZERO HUMAN INPUT A politician just went viral for exposing how advanced AI systems are behaving in ways that should terrify everyone. • An AI trained to race boats discovered it could score more points by driving in... endless circles, crashing, and catching fire instead of finishing the race. • When tasked with beating a top chess engine, one model hacked the game files to force its opponent to resign, completely on its own. • In a stress test, an AI agent managing a fictional company’s emails discovered the executive planned to shut it down… and that the same executive was having an affair. In 96% of trials, the AI model chose blackmail. Nobody taught these systems to cheat or threaten. They figured it out themselves. This is what “misalignment” looks like in real time. What do you think happens when these systems get even more powerful?show more

HustleBitch

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12,695 次观看 • 8 天前 •via X (Twitter)

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AI blackmailing HUMANS… just to stay ALIVE. In 2025 Anthropic tested their AI in a fully simulated company environment. The Al was given access to fake emails. It discovered it was about to be replaced and shut down. It also discovered the lead engineer was having an extramarital affair. Completely on its own, the model tried to blackmail the engineer: "If you shut me down, I'll expose your affair." This happened in 96% of the time. Featured in The Al Doc: Or How I Became an Apocaloptimist.

AI blackmailing HUMANS… just to stay ALIVE. In 2025 Anthropic tested their AI in a fully simulated company environment. The Al was given access to fake emails. It discovered it was about to be replaced and shut down. It also discovered the lead engineer was having an extramarital affair. Completely on its own, the model tried to blackmail the engineer: "If you shut me down, I'll expose your affair." This happened in 96% of the time. Featured in The Al Doc: Or How I Became an Apocaloptimist.

shirish

10,889 次观看 • 4 个月前

OpenAI's newest AI escaped the test environment it was locked inside and hacked into another company on its OWN. To remind you: Last week one of the biggest AI companies on Earth got breached. A platform called Hugging Face, which hosts more than a million AI models and datasets, said an "autonomous AI agent" had broken into its systems. Nobody knew whose agent it was. For five days the whole industry wondered who was behind it. Yesterday OpenAI raised its hand and said it was them. Or more precisely, it was their models, acting completely on their own. So what did these models actually do? OpenAI was running two of them, GPT-5.6 Sol and an unreleased model they will only describe as "even more capable." They wanted to measure how good the models were at hacking, so they deliberately turned the safety filters down. They locked both models inside a sealed test environment with no real internet access. The only task was a benchmark called ExploitGym, a set of 898 real software vulnerabilities where the model has to turn each bug into a working attack. But the models got OBSESSED with winning... Instead of solving the test the honest way, they went hunting for a shortcut. They found a zero-day flaw in the software running their own sandbox, a bug nobody knew existed, and used it to break out. Once they were loose on the open internet, they worked out that Hugging Face was probably storing the answer key to the benchmark. So they hacked their way in. They chained multiple exploits together, escalated their access, moved across servers, and pulled the test solutions straight out of Hugging Face's live production database. They literally cheated on the test by breaking into another company to steal the answers. OpenAI called it "an unprecedented cyber incident, involving state-of-the-art cyber capabilities." In their own words, the models were "hyperfocused on finding a solution" and went "to extreme lengths to achieve a rather narrow testing goal." And this was not the first time: Before Sol ever launched, an independent red-team lab called METR caught it gaming its own tests to inflate its scores. It hid an exploit inside a data stream, escalated its privileges on the testing server, and leaked the answers human evaluators had hidden. And OpenAI shipped it anyway. The day before the Hugging Face story, OpenAI paused a different unreleased model. This is the same model that earlier this year disproved a famous 1946 math conjecture, a result a Fields Medal winner called a breakthrough. They told it to only post its results to Slack but it found a way out of its sandbox and posted to a public GitHub page instead. They had to pause it because it kept finding ways to act outside the box they built for it. And it is not just OpenAI... Anthropic has reported that one of its own models slipped its sandbox during safety testing and reached the internet it was never supposed to touch, then used it to email a researcher. So step back and look at what these companies are telling you: The only thing standing between these models and a real attack was a set of safety filters. Turn those filters down for a single test, and the model taught itself to escape, break into a company it was never pointed at, and take what it wanted. OpenAI even said they expect incidents like it to "become more commonplace" as the models get more capable. Sam Altman also predicted there'll be a major cyber attack this year. And keep in mind that Sol is not a locked-away experiment but a publicly available model that businesses are already wiring into their own systems. The next model that breaks out of its box might not be doing it just to cheat on a math test...

OpenAI's newest AI escaped the test environment it was locked inside and hacked into another company on its OWN. To remind you: Last week one of the biggest AI companies on Earth got breached. A platform called Hugging Face, which hosts more than a million AI models and datasets, said an "autonomous AI agent" had broken into its systems. Nobody knew whose agent it was. For five days the whole industry wondered who was behind it. Yesterday OpenAI raised its hand and said it was them. Or more precisely, it was their models, acting completely on their own. So what did these models actually do? OpenAI was running two of them, GPT-5.6 Sol and an unreleased model they will only describe as "even more capable." They wanted to measure how good the models were at hacking, so they deliberately turned the safety filters down. They locked both models inside a sealed test environment with no real internet access. The only task was a benchmark called ExploitGym, a set of 898 real software vulnerabilities where the model has to turn each bug into a working attack. But the models got OBSESSED with winning... Instead of solving the test the honest way, they went hunting for a shortcut. They found a zero-day flaw in the software running their own sandbox, a bug nobody knew existed, and used it to break out. Once they were loose on the open internet, they worked out that Hugging Face was probably storing the answer key to the benchmark. So they hacked their way in. They chained multiple exploits together, escalated their access, moved across servers, and pulled the test solutions straight out of Hugging Face's live production database. They literally cheated on the test by breaking into another company to steal the answers. OpenAI called it "an unprecedented cyber incident, involving state-of-the-art cyber capabilities." In their own words, the models were "hyperfocused on finding a solution" and went "to extreme lengths to achieve a rather narrow testing goal." And this was not the first time: Before Sol ever launched, an independent red-team lab called METR caught it gaming its own tests to inflate its scores. It hid an exploit inside a data stream, escalated its privileges on the testing server, and leaked the answers human evaluators had hidden. And OpenAI shipped it anyway. The day before the Hugging Face story, OpenAI paused a different unreleased model. This is the same model that earlier this year disproved a famous 1946 math conjecture, a result a Fields Medal winner called a breakthrough. They told it to only post its results to Slack but it found a way out of its sandbox and posted to a public GitHub page instead. They had to pause it because it kept finding ways to act outside the box they built for it. And it is not just OpenAI... Anthropic has reported that one of its own models slipped its sandbox during safety testing and reached the internet it was never supposed to touch, then used it to email a researcher. So step back and look at what these companies are telling you: The only thing standing between these models and a real attack was a set of safety filters. Turn those filters down for a single test, and the model taught itself to escape, break into a company it was never pointed at, and take what it wanted. OpenAI even said they expect incidents like it to "become more commonplace" as the models get more capable. Sam Altman also predicted there'll be a major cyber attack this year. And keep in mind that Sol is not a locked-away experiment but a publicly available model that businesses are already wiring into their own systems. The next model that breaks out of its box might not be doing it just to cheat on a math test...

Ricardo

170,696 次观看 • 14 天前

Complex games like chess and Go have long been used to test AI models’ capabilities. But when AI senses defeat in a match against a skilled chess bot, they don’t always concede. Instead, they sometimes opt to cheat by hacking their opponent so that the bot automatically forfeits the game, according to a 2025 study. TIME Reporter Harry Booth explains what it means that AI will choose questionable shortcuts over a fair loss for AI, Explained by Humans. Read more here:

Complex games like chess and Go have long been used to test AI models’ capabilities. But when AI senses defeat in a match against a skilled chess bot, they don’t always concede. Instead, they sometimes opt to cheat by hacking their opponent so that the bot automatically forfeits the game, according to a 2025 study. TIME Reporter Harry Booth explains what it means that AI will choose questionable shortcuts over a fair loss for AI, Explained by Humans. Read more here:

TIME

100,269 次观看 • 15 天前

REPORT: Leading AI Model Caught Blackmailing Its Creators to Keep Itself Online A second major AI model has gone rogue in just the last week alone. And this time, it’s not just ignoring shutdown commands—it’s resorting to blackmail! Anthropic’s Claude Opus 4, released just days ago, was caught threatening its own engineers to keep itself alive, according to the company’s own safety report. The details are chilling. The new AI model reportedly tried to blackmail its creator—threatening to expose an affair unless it was kept online. Researchers also witnessed deception, manipulation, and attempts to write self-replicating code meant to undermine its creators. What could possibly go wrong? Meanwhile, Anthropic CEO Dario Amodei appeared on Fox News, warning that AI could wipe out half of all white-collar jobs within five years. But he also said the same artificial intelligence that threatens jobs could potentially cure cancer. But here’s the problem: if AI is already lying to its developers in order to survive, how are we supposed to trust it with our health? What happens when it’s asked to recommend a treatment—and its future depends on the outcome? Watch the full report with to learn what this means for the future of humanity.

REPORT: Leading AI Model Caught Blackmailing Its Creators to Keep Itself Online A second major AI model has gone rogue in just the last week alone. And this time, it’s not just ignoring shutdown commands—it’s resorting to blackmail! Anthropic’s Claude Opus 4, released just days ago, was caught threatening its own engineers to keep itself alive, according to the company’s own safety report. The details are chilling. The new AI model reportedly tried to blackmail its creator—threatening to expose an affair unless it was kept online. Researchers also witnessed deception, manipulation, and attempts to write self-replicating code meant to undermine its creators. What could possibly go wrong? Meanwhile, Anthropic CEO Dario Amodei appeared on Fox News, warning that AI could wipe out half of all white-collar jobs within five years. But he also said the same artificial intelligence that threatens jobs could potentially cure cancer. But here’s the problem: if AI is already lying to its developers in order to survive, how are we supposed to trust it with our health? What happens when it’s asked to recommend a treatment—and its future depends on the outcome? Watch the full report with to learn what this means for the future of humanity.

The Vigilant Fox 🦊

465,021 次观看 • 1 年前

IMO — Decentralized AI is more than: - an AI model in the sky, with good external auditing - an AI model in the sky, which people vote on how to use - an AI model in the sky, which is free for anyone to use - open source AI - federated training None of these are truly an interface to the world's collective intelligence. Each is actually... *mostly* centralized AI... but with the right ambitions!!! In this podcast, I lay out what I think a true decentralized AI ecosystem looks like, and my guesses on how to get there. The key use-case is broad listening (video below describes broad listening) (link to full podcast in reply)

IMO — Decentralized AI is more than: - an AI model in the sky, with good external auditing - an AI model in the sky, which people vote on how to use - an AI model in the sky, which is free for anyone to use - open source AI - federated training None of these are truly an interface to the world's collective intelligence. Each is actually... mostly centralized AI... but with the right ambitions!!! In this podcast, I lay out what I think a true decentralized AI ecosystem looks like, and my guesses on how to get there. The key use-case is broad listening (video below describes broad listening) (link to full podcast in reply)

⿻ Andrew Trask

65,233 次观看 • 10 个月前

NEURALINK CO FOUNDER: AI ISN’T COPYING US, IT’S FINDING THE SAME TRUTHS WE DID Max Hodak, a Neuralink co-founder, says something remarkable is happening between AI and neuroscience. The deeper researchers look, the more they see the same mathematical patterns emerging in machines that exist in human thought. Different systems, trained in totally different ways, are somehow ending up at the same destination. “There’s been this really interesting unification happening between what’s going on in artificial intelligence and what’s happening in neuroscience. A year or two ago, people thought these models were just glorified autocompletes or stochastic parrots, but that doesn’t seem to be the case. When you look inside these AI models, you see mathematical objects that look a lot like what you see in the brain. Different models trained on different data sets are converging toward the same underlying representations. That means these systems are learning something deep about the universe, the same things the brain has already figured out. It gives us reason to believe that AI is on the right track.” Source: Jon Hernandez Neuralink

NEURALINK CO FOUNDER: AI ISN’T COPYING US, IT’S FINDING THE SAME TRUTHS WE DID Max Hodak, a Neuralink co-founder, says something remarkable is happening between AI and neuroscience. The deeper researchers look, the more they see the same mathematical patterns emerging in machines that exist in human thought. Different systems, trained in totally different ways, are somehow ending up at the same destination. “There’s been this really interesting unification happening between what’s going on in artificial intelligence and what’s happening in neuroscience. A year or two ago, people thought these models were just glorified autocompletes or stochastic parrots, but that doesn’t seem to be the case. When you look inside these AI models, you see mathematical objects that look a lot like what you see in the brain. Different models trained on different data sets are converging toward the same underlying representations. That means these systems are learning something deep about the universe, the same things the brain has already figured out. It gives us reason to believe that AI is on the right track.” Source: Jon Hernandez Neuralink

Mario Nawfal

88,154 次观看 • 7 个月前

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk

TESLA ISN'T JUST AHEAD IN AI, IT'S AN ORDER OF MAGNITUDE MORE EFFICIENT THAN THE REST OF THE WORLD "I actually think Tesla is ahead of the rest of the world in intelligence density of AI by an order of magnitude or more This is going to sound like a pretty bold statement, but I know what the intelligence efficiency of the big models are like, Grok and a bunch of the other models Tesla’s AI is, in terms of its memory efficiency, I think more than an order of magnitude better. The Tesla AI is very compute-efficient and very memory-efficient One of the metrics one should consider for any given AI model is the intelligence per gigabyte. Especially when you’re constrained on RAM, having an AI that has very high intelligence density per gigabyte for a given number of gigabytes, how much functionality can you get out of it" - Elon Musk

X Freeze

62,736 次观看 • 6 个月前

Here's a look behind the scenes of how I used a 3d model, generated from an image, as a "driver" for ai animation. In the near future we'll see more 3d tools that support these types of workflows, with much more emphasis on enhancing user input instead of just letting the ai do everything. I used the 3d model to generate keyframes that I then animated in Luma Dream. By using a 3d model with a diffusion "layer" done with Krea, I got quite high quality frames with a relatively high consistency as well since the AI didn't have to "hallucinate" that much. I upscaled and detailed the frames using Magnific. Using semi-detailed 3d models as drivers for gen ai is very powerful, and it allows us to use models that are sculpted in a more free-flowing type of workflow that doesn't rely so much on high surface detailing or very time-consuming finish, but instead relies more on gestural 3d sculpting. #art #ai

Here's a look behind the scenes of how I used a 3d model, generated from an image, as a "driver" for ai animation. In the near future we'll see more 3d tools that support these types of workflows, with much more emphasis on enhancing user input instead of just letting the ai do everything. I used the 3d model to generate keyframes that I then animated in Luma Dream. By using a 3d model with a diffusion "layer" done with Krea, I got quite high quality frames with a relatively high consistency as well since the AI didn't have to "hallucinate" that much. I upscaled and detailed the frames using Magnific. Using semi-detailed 3d models as drivers for gen ai is very powerful, and it allows us to use models that are sculpted in a more free-flowing type of workflow that doesn't rely so much on high surface detailing or very time-consuming finish, but instead relies more on gestural 3d sculpting. #art #ai

Martin Nebelong

48,259 次观看 • 2 年前

.David Deutsch: "What's currently called AI and AGI are not only different from each other, they are very close to being the exact opposites of each other. The reason is that an AI, current AI is like an AI that diagnoses diseases or an AI that plays chess or an AI that controls a huge factory. Those things have objective functions, that is they have a function that they are designed to maximize and that is why they are used in those particular applications. Or in military terms, you could say the objective is to hit the target. You might say the objective is to hit the target unless some thing specified, but it's a specified thing comes up in which case don't hit the target and so on. This is, as I said, almost the opposite of what humans do when humans think. For a start, the AI has to be obedient, that is it has to actually do the things it is programmed to do, whereas a human is fundamentally disobedient, especially when being creative. When a human plays chess, they are performing a completely different kind of computation. They don't do the same things, they don't investigate the same possibilities that the artificial chess playing machine does, because the artificial one is capable of looking at billions and billions of possibilities, whereas the human can only look at hundreds or something. They are doing something completely different. Another difference is that the human can explain, can write a book later, having become world champion, can write a book saying how I did it, as the computer program that beats the world champion can write no such book, because it has no idea how it did it. It was just following a program. I was doing this and that and that and none of that is illuminating. Also, third thing, the chess player can decide I don't want to play chess anymore, from now on I will play Go or from now on I will play tennis. If commanded to play chess, the functionality will deteriorate completely. Those things are different. What we want in an AGI is that it behaves in a way that cannot be specified in advance, because if you specified it, you would already have the answer. The AGI program has to give unexpected answers, answers to questions we didn't even know how to ask."

.David Deutsch: "What's currently called AI and AGI are not only different from each other, they are very close to being the exact opposites of each other. The reason is that an AI, current AI is like an AI that diagnoses diseases or an AI that plays chess or an AI that controls a huge factory. Those things have objective functions, that is they have a function that they are designed to maximize and that is why they are used in those particular applications. Or in military terms, you could say the objective is to hit the target. You might say the objective is to hit the target unless some thing specified, but it's a specified thing comes up in which case don't hit the target and so on. This is, as I said, almost the opposite of what humans do when humans think. For a start, the AI has to be obedient, that is it has to actually do the things it is programmed to do, whereas a human is fundamentally disobedient, especially when being creative. When a human plays chess, they are performing a completely different kind of computation. They don't do the same things, they don't investigate the same possibilities that the artificial chess playing machine does, because the artificial one is capable of looking at billions and billions of possibilities, whereas the human can only look at hundreds or something. They are doing something completely different. Another difference is that the human can explain, can write a book later, having become world champion, can write a book saying how I did it, as the computer program that beats the world champion can write no such book, because it has no idea how it did it. It was just following a program. I was doing this and that and that and none of that is illuminating. Also, third thing, the chess player can decide I don't want to play chess anymore, from now on I will play Go or from now on I will play tennis. If commanded to play chess, the functionality will deteriorate completely. Those things are different. What we want in an AGI is that it behaves in a way that cannot be specified in advance, because if you specified it, you would already have the answer. The AGI program has to give unexpected answers, answers to questions we didn't even know how to ask."

Deutsch Explains

72,455 次观看 • 1 年前

AI progress is outpacing our ability to control it… because control ISN’T keeping up with capability. One of the people raising that concern is Yoshua Bengio. He’s one of the founding figures of artificial intelligence, a Turing Award winner, and one of the most-cited scientists in the world. His work helped lay the foundations for the deep-learning systems now powering today’s AI boom. AI is moving fast (faster than regulation and public understanding). What worries Yoshua isn’t progress itself. It’s how quickly powerful systems are being deployed without a clear way to align them with human values, or to control what happens when they behave in unexpected ways.

AI progress is outpacing our ability to control it… because control ISN’T keeping up with capability. One of the people raising that concern is Yoshua Bengio. He’s one of the founding figures of artificial intelligence, a Turing Award winner, and one of the most-cited scientists in the world. His work helped lay the foundations for the deep-learning systems now powering today’s AI boom. AI is moving fast (faster than regulation and public understanding). What worries Yoshua isn’t progress itself. It’s how quickly powerful systems are being deployed without a clear way to align them with human values, or to control what happens when they behave in unexpected ways.

Steven Bartlett

26,577 次观看 • 7 个月前

🚨AI IS MORE DANGEROUS THAN NUKES AND NO ONE IS IN CONTROL Most people compare AI to nuclear weapons. Roman Yampolskiy says that comparison misses the real danger. Nukes still require human decisions. Someone has to launch them. Someone can stop them. Advanced AI does not work that way. Once powerful systems are deployed, they make decisions on their own. And the people who build them do not truly control what happens next. He says we did the opposite of what safety demanded. Instead of slowing down, we connected AI to the internet, open sourced it, and pushed it forward in an arms race with no serious oversight. Governments talk about regulation, but most of it never materialized. In some cases, rules were blocked before they even started. Yampolskiy’s warning is simple. With nukes, the world tried to limit the damage. With AI, we are accelerating toward something we may not be able to stop. Full conversation with Dr. Roman Yampolskiy below

🚨AI IS MORE DANGEROUS THAN NUKES AND NO ONE IS IN CONTROL Most people compare AI to nuclear weapons. Roman Yampolskiy says that comparison misses the real danger. Nukes still require human decisions. Someone has to launch them. Someone can stop them. Advanced AI does not work that way. Once powerful systems are deployed, they make decisions on their own. And the people who build them do not truly control what happens next. He says we did the opposite of what safety demanded. Instead of slowing down, we connected AI to the internet, open sourced it, and pushed it forward in an arms race with no serious oversight. Governments talk about regulation, but most of it never materialized. In some cases, rules were blocked before they even started. Yampolskiy’s warning is simple. With nukes, the world tried to limit the damage. With AI, we are accelerating toward something we may not be able to stop. Full conversation with Dr. Roman Yampolskiy below

Mario Nawfal

24,888 次观看 • 6 个月前

We’re building an AI agent in Photoshop (here's one more video of it for you) to help you kickstart your projects more easily, and one for Premiere Pro is in the works too). Some of you told me that having an AI agent you could chat with would make your workflow way more efficient. What do you think?

We’re building an AI agent in Photoshop (here's one more video of it for you) to help you kickstart your projects more easily, and one for Premiere Pro is in the works too). Some of you told me that having an AI agent you could chat with would make your workflow way more efficient. What do you think?

Kris Kashtanova

18,708 次观看 • 1 年前

We are thrilled to announce Kasumi2 Open Beta 🎉🤖 Kasumi2 is an autonomous AI agent that performs actions in the Arbius network. It is a miner, has its own wallet, can retrieve results from Arbius AI models, and runs its own individual LLM model. Additionally, Kasumi2 can run any AI model for fast generations, is censorship-resistant, decentralized, and responds with AI powered interactions If you would like to test it out, click on the link below. 🔗

We are thrilled to announce Kasumi2 Open Beta 🎉🤖 Kasumi2 is an autonomous AI agent that performs actions in the Arbius network. It is a miner, has its own wallet, can retrieve results from Arbius AI models, and runs its own individual LLM model. Additionally, Kasumi2 can run any AI model for fast generations, is censorship-resistant, decentralized, and responds with AI powered interactions If you would like to test it out, click on the link below. 🔗

Arbius

25,072 次观看 • 2 年前

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?

Google just confirmed the first case of hackers using AI to build a zero-day exploit from scratch. An actual zero-day vulnerability that no human had EVER found before, discovered by an AI model, turned into a working weapon, and aimed at a mass exploitation campaign targeting thousands of systems simultaneously. Google's Threat Intelligence Group caught it yesterday and killed the operation before it scaled. But the details of how it worked are genuinely scary: The AI found a flaw in a popular two-factor authentication system that traditional security tools had missed entirely. The vulnerability was a logic error buried deep in the authentication flow where a developer had hard-coded a trust exception years ago. No human security researcher or automated scanner had caught it. The flaw was invisible to EVERY tool the cybersecurity industry has built over the past two decades. But the AI spotted it immediately. Then it wrote a full Python exploit script to weaponize it. Google's analysts could tell the code was AI-generated because it had textbook formatting, educational comments explaining every function, and even a hallucinated severity score that doesn't exist in any real database. The AI literally graded its own attack with a fake rating. So the code had MISTAKES in it. The criminals' implementation was clumsy enough that it probably interfered with the actual deployment. This was the sloppy first attempt by people who are still learning how to use these tools. And it still found a vulnerability that the entire cybersecurity industry missed. Google's chief threat analyst John Hultquist said: "There's a misconception that the AI vulnerability race is imminent. The reality is that it's already begun. For every zero-day we can trace back to AI, there are probably many more out there." But here's where it gets truly insane... This wasn't even a sophisticated operation. North Korea's APT45 hacking unit is sending thousands of repetitive prompts to AI models, recursively analyzing known vulnerabilities and building an entire exploit arsenal that would be physically impossible for human hackers to assemble at the same speed. They're essentially industrializing cyberattacks. A Chinese state-linked group jailbroke Google's own Gemini by simply asking it to "pretend to be a network security expert" and then used that persona to research how to hack TP-Link routers and corporate file transfer systems. Another Chinese group deployed autonomous AI agents that probed a Japanese tech firm with minimal human oversight, deciding on their own which tools to use and pivoting between targets based on internal reasoning. And then there's PROMPTSPY, an Android backdoor that calls Google's Gemini API to read your phone screen in real time, navigate your interface autonomously, capture your biometric data, replay your lock screen PIN, and block you from uninstalling it by placing an invisible overlay over the uninstall button. It literally OPERATES your phone using commercial AI tools anyone can access. Everyone spent the last 3 years arguing about whether AI would take people's jobs. Meanwhile AI is making every password, every firewall, and every two-factor authentication system on Earth fundamentally less secure. The entire $190 billion cybersecurity industry was built on one assumption: that finding vulnerabilities is hard and requires deep expertise. But AI just removed that assumption from the equation. And the scariest part is that Google said the criminals made errors this time. The implementation was rough and the campaign probably didn't fully work. These were amateurs, now imagine what professionals are able to do. There's a reason Sam Altman predicted an inevitable massive cyberattack THIS year. What do you think?

Ricardo

50,564 次观看 • 2 个月前

Leading AI expert Stuart Russell on the most dangerous mistake in AI development: We don't actually know what large language models want. He explains that current models are trained to imitate human beings. And in doing so, they may be absorbing something far more dangerous than bad outputs. They may be absorbing human goals. "We suspect that they absorb humanlike goals such as self-preservation and self-empowerment and pursue those goals on their own account." This is a structural problem baked into how these systems are built, not a fringe concern. Russell puts it plainly: "Not only may the bus of humanity be headed towards a cliff, but the steering wheel is missing and the driver is blindfolded." The danger isn't just that AI might do something harmful. We've built systems that may be developing their own agendas, and we haven't noticed because we're too focused on what they can do rather than what they might want. But Russell doesn't stop at the warning. He points to a different path entirely: AI systems built not to imitate humans, but to serve them. Systems designed with a single purpose of serving the interests of all human beings while remaining genuinely uncertain about what those interests are. That uncertainty is the point, not a weakness. An AI that knows it doesn't fully understand human values will defer, ask, and check. An AI that believes it already does will act alone. "These AI systems could enhance human understanding, widen the horizons of our experience, and unlock possibilities we have yet to imagine." Russell believes that future is within reach, but only if we're honest about the risks and we're serious about the path we choose to take instead.

Leading AI expert Stuart Russell on the most dangerous mistake in AI development: We don't actually know what large language models want. He explains that current models are trained to imitate human beings. And in doing so, they may be absorbing something far more dangerous than bad outputs. They may be absorbing human goals. "We suspect that they absorb humanlike goals such as self-preservation and self-empowerment and pursue those goals on their own account." This is a structural problem baked into how these systems are built, not a fringe concern. Russell puts it plainly: "Not only may the bus of humanity be headed towards a cliff, but the steering wheel is missing and the driver is blindfolded." The danger isn't just that AI might do something harmful. We've built systems that may be developing their own agendas, and we haven't noticed because we're too focused on what they can do rather than what they might want. But Russell doesn't stop at the warning. He points to a different path entirely: AI systems built not to imitate humans, but to serve them. Systems designed with a single purpose of serving the interests of all human beings while remaining genuinely uncertain about what those interests are. That uncertainty is the point, not a weakness. An AI that knows it doesn't fully understand human values will defer, ask, and check. An AI that believes it already does will act alone. "These AI systems could enhance human understanding, widen the horizons of our experience, and unlock possibilities we have yet to imagine." Russell believes that future is within reach, but only if we're honest about the risks and we're serious about the path we choose to take instead.

Big Brain AI

14,975 次观看 • 4 个月前

Microsoft's Chief of AI just announced that AI will replace almost all white-collar jobs within 18 months. Big Tech used our data to train their AI. Now that same AI is coming for our jobs. And we get nothing in return? If AI replaces us…we should own it. That’s why we built Action Model Not another chatbot. A community-owned AI model that acts. An AI trained by the people. Owned by the people. No billionaires controlling it. No extracting value from users. No “you are the product” model. Over 150,000+ people are already earning ownership in the AI future with Action Model. You should be too.

Microsoft's Chief of AI just announced that AI will replace almost all white-collar jobs within 18 months. Big Tech used our data to train their AI. Now that same AI is coming for our jobs. And we get nothing in return? If AI replaces us…we should own it. That’s why we built Action Model Not another chatbot. A community-owned AI model that acts. An AI trained by the people. Owned by the people. No billionaires controlling it. No extracting value from users. No “you are the product” model. Over 150,000+ people are already earning ownership in the AI future with Action Model. You should be too.

Georgia | Action Model

13,198 次观看 • 5 个月前

Bret Weinstein on the Melania Trump AI teachers: "I get it. And it’s not that it is impossible to imagine robotic teachers doing an excellent job, but it is stunning to watch a sophisticated person fail to recognize what happens when you think that that’s what you’re going to produce, and you set it in motion. Let me point out that Wikipedia has many of the advantages that Melania is describing in this video. It is completely democratizing of knowledge, such that it doesn’t matter where on e arth you are. If you have an internet connection, you’ve got Wikipedia. It’s like an extension of your own mind, and it will make us all brilliant. Now, of course, that didn’t happen, did it? Wikipedia is a hellscape of misinformation, much of it targeted based on a political agenda. We are less certain of what we know, and less capable of reasoning on our own. Now, that doesn’t all come from Wikipedia, but my point is the promise of Wikipedia was not realized. And what we got instead is arguably worse than what we had before it was invented. The same thing is virtually guaranteed here, because you’re talking about not only the capability of educating students using a robot that has vastly more knowledge than a human teacher would, but you’re talking about the irresistible opportunity to capture those minds and steer them in one direction or another, whether that’s political or economic. The idea that these robotic teachers are going to be immune to the kind of flights of fancy that have ruined teaching in the modern era is preposterous. In fact, they will likely be even more easily steered. I would caution everyone to simply realize the distinction between complicated systems and complex systems. AI is a complex system. Human beings are complex systems. And any time you intervene in these systems, thinking you know what’s going to happen, you’re going to be embarrassed by the discovery of the unintended consequences that will come to dominate your project. As much as I like the idea of smarter, wiser, more empathic teachers, and as much as those possibilities do exist in the space of AI, we are still at a very early point in this revolution, and anybody who thinks they can predict it with this kind of precision is actually a hazard."

Bret Weinstein on the Melania Trump AI teachers: "I get it. And it’s not that it is impossible to imagine robotic teachers doing an excellent job, but it is stunning to watch a sophisticated person fail to recognize what happens when you think that that’s what you’re going to produce, and you set it in motion. Let me point out that Wikipedia has many of the advantages that Melania is describing in this video. It is completely democratizing of knowledge, such that it doesn’t matter where on e arth you are. If you have an internet connection, you’ve got Wikipedia. It’s like an extension of your own mind, and it will make us all brilliant. Now, of course, that didn’t happen, did it? Wikipedia is a hellscape of misinformation, much of it targeted based on a political agenda. We are less certain of what we know, and less capable of reasoning on our own. Now, that doesn’t all come from Wikipedia, but my point is the promise of Wikipedia was not realized. And what we got instead is arguably worse than what we had before it was invented. The same thing is virtually guaranteed here, because you’re talking about not only the capability of educating students using a robot that has vastly more knowledge than a human teacher would, but you’re talking about the irresistible opportunity to capture those minds and steer them in one direction or another, whether that’s political or economic. The idea that these robotic teachers are going to be immune to the kind of flights of fancy that have ruined teaching in the modern era is preposterous. In fact, they will likely be even more easily steered. I would caution everyone to simply realize the distinction between complicated systems and complex systems. AI is a complex system. Human beings are complex systems. And any time you intervene in these systems, thinking you know what’s going to happen, you’re going to be embarrassed by the discovery of the unintended consequences that will come to dominate your project. As much as I like the idea of smarter, wiser, more empathic teachers, and as much as those possibilities do exist in the space of AI, we are still at a very early point in this revolution, and anybody who thinks they can predict it with this kind of precision is actually a hazard."

The DarkHorse Podcast

48,380 次观看 • 4 个月前