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Introducing XSS2Shell ⛓️: WordPress Core preauth XSS→RCE vulnerability affecting 43% of the internet, discovered autonomously by pwn (using open-source models), affecting all WordPress versions. Please patch CVE-2026-64638 as soon as possible!

pwn.ai

9,577 subscribers

1,647,376 次观看 • 1 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

25 条评论

Nav Toor1 个月前

found by open-source models, not some big lab

NodeArc 的头像

NodeArc1 个月前

Open source models defying closed frontier models in cybersec will never be uncool

|| Ɒraɢσn || 的头像

|| Ɒraɢσn ||1 个月前

.!

LEX 的头像

LEX1 个月前

Morris worm, 1988: 10 percent of the internet, one grad student, one sendmail bug. 38 years later an autonomous agent covers a bigger surface for the price of a compute run. The finder collapsed to zero.

Syed Muhammad Ali Minhal🔻 的头像

Syed Muhammad Ali Minhal🔻1 个月前

“43 efffng % of the internet !! “

Fiducial.ai 的头像

Fiducial.ai1 个月前

is there a disclosure timeline? preauth rce on core found by a model is a first

安叫兽|Bird🕊️ 🔶 BNB 的头像

安叫兽|Bird🕊️ 🔶 BNB1 个月前

这影响面也太离谱了，赶紧打补丁

Zill | Tech Support & Cybersec 的头像

Zill | Tech Support & Cybersec1 个月前

@HackingDave Pre-auth XSS straight to RCE on core WordPress is wild. The fact that an open-source model chain found this autonomously is huge for AI red teaming. Patching ASAP. 🛡️

icefrog.◎ 的头像

icefrog.◎1 个月前

43% is wild.

Gobena Dache (ጎበና ዳጬ) 的头像

Gobena Dache (ጎበና ዳጬ)1 个月前

This is very huge

cyberfreak999 的头像

cyberfreak9991 个月前

It's really interesting that the chain was surfaced by open source models. The harder part for most operators will be confirming whether thier auto update path actually pulled the backports to older major versions before the first phishing wave starts.

Hải Ly 的头像

Hải Ly1 个月前

43 percent is a nightmare. manual audits are officially dead, time to automate the defense

Dyu Ninja Lead 的头像

Dyu Ninja Lead1 个月前

đỉnh thiệt, patch gấp đi ae

sutanisurabu 的头像

sutanisurabu1 个月前

Пиздец

⸎Medusa⸎𓆙 ϬɸяϬɸήSS Ϭ⧬đđЗŝŝ ⸎Serpent Ƥя¡ήcεşş𓆙⧪𖤐 的头像

⸎Medusa⸎𓆙 ϬɸяϬɸήSS Ϭ⧬đđЗŝŝ ⸎Serpent Ƥя¡ήcεşş𓆙⧪𖤐1 个月前

these wordpress breaches have been active for a while and they keep getting worse. good luck LMAO

Life Makers Team🍉🇵🇸 的头像

Life Makers Team🍉🇵🇸1 个月前

Your donation brings joy and happiness to every child affected 💗🤲💗🤲by trauma in Gaza. Be a support for them and contribute even just one dollar.

Leliu 的头像

Leliu1 个月前

Concerning vulnerability impacting a significant portion of the internet.

snsn 的头像

snsn1 个月前

where did she buy that outfit from

Safwan 的头像

Safwan1 个月前

43% WP claim is huge, patch fast.

Robinhood Whale 🐋 的头像

Robinhood Whale 🐋1 个月前

ONLY UP FROM HERE MOON IS Programmed 🌒🚀

Dalal 🫎 的头像

Dalal 🫎1 个月前

still in his prime

Kenneth S. 的头像

Kenneth S.1 个月前

43% of the internet and “patch as soon as possible” in the same sentence is terrifying. AI-assisted security research is moving fast.

Papa Lumi 的头像

Papa Lumi1 个月前

Tôi cũng từng gặp phải lỗ hổng đó 😳, làm cách nào bạn tự động tìm thấy nó?

Jan Grewe 的头像

Jan Grewe1 个月前

If a logged in(!) administrator(!) needs to explicitly click(!) something, is it really a "preauth RCE"? Or are we just making shit up now, as one would expect from somebody with "AI" in their name? Damn clanker wankers...

ovax 的头像

ovax1 个月前

Wp2shell et maintenant xss2shell 🤣🤣🤣

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93,416 次观看 • 1 个月前

Recently, I discovered a campaign spreading via Telegram Mini Apps. In fact, a threat actor attempted to phish me using this tactic.👇 The technique is highly convincing because it involves a trust boundary violation in Telegram’s MiniApp SDK (affecting Android, Desktop, iOS, and Web versions). Telegram allows third-party code to use its trusted native UI through showPopup(), but it does so without clearly indicating the source to the user or providing safeguards against abuse. This constitutes a design weakness, as the triggered pop-up belongs to Telegram and can be used in an arbitrary manner. -The attacker misleads the user by leveraging Telegram’s own native UI component. -The popup is rendered outside the MiniApp iframe, within Telegram’s own UI layer. -The user perceives it as a legitimate warning from Telegram itself. -By placing a single button, the attacker can redirect the user to a phishing page. -Since the phishing page opens inside the Mini App, the phishing URL is not visibly exposed. After conducting detailed research, I simulated this attack and demonstrated it in the video below. Be cautious when opening Mini Apps in bots.

Recently, I discovered a campaign spreading via Telegram Mini Apps. In fact, a threat actor attempted to phish me using this tactic.👇 The technique is highly convincing because it involves a trust boundary violation in Telegram’s MiniApp SDK (affecting Android, Desktop, iOS, and Web versions). Telegram allows third-party code to use its trusted native UI through showPopup(), but it does so without clearly indicating the source to the user or providing safeguards against abuse. This constitutes a design weakness, as the triggered pop-up belongs to Telegram and can be used in an arbitrary manner. -The attacker misleads the user by leveraging Telegram’s own native UI component. -The popup is rendered outside the MiniApp iframe, within Telegram’s own UI layer. -The user perceives it as a legitimate warning from Telegram itself. -By placing a single button, the attacker can redirect the user to a phishing page. -Since the phishing page opens inside the Mini App, the phishing URL is not visibly exposed. After conducting detailed research, I simulated this attack and demonstrated it in the video below. Be cautious when opening Mini Apps in bots.

0x6rss

49,167 次观看 • 7 个月前

The network for machine intelligence Two years ago, we laid out our vision for a machine learning compute protocol. One that connects every device in the world into an open network for machine intelligence, with no gatekeepers or artificial boundaries. This week, we’ll be sharing some of our early progress, beginning with RL Swarm, a peer-to-peer system for collaborative reinforcement learning over the internet. Next month, we’ll open our Testnet, allowing anyone to contribute to the frontier of open machine intelligence. Introducing RL Swarm RL Swarm is a fully open source system for collaborative reinforcement learning over the internet. It is a live demo of our research findings, which show that models training with RL learn faster when they train as a collective swarm than they do on their own. Join our swarm now to see this in practice. You can participate with consumer hardware at home or a powerful GPU in the cloud. You can follow along with the swarm’s progress by following the links below.

The network for machine intelligence Two years ago, we laid out our vision for a machine learning compute protocol. One that connects every device in the world into an open network for machine intelligence, with no gatekeepers or artificial boundaries. This week, we’ll be sharing some of our early progress, beginning with RL Swarm, a peer-to-peer system for collaborative reinforcement learning over the internet. Next month, we’ll open our Testnet, allowing anyone to contribute to the frontier of open machine intelligence. Introducing RL Swarm RL Swarm is a fully open source system for collaborative reinforcement learning over the internet. It is a live demo of our research findings, which show that models training with RL learn faster when they train as a collective swarm than they do on their own. Join our swarm now to see this in practice. You can participate with consumer hardware at home or a powerful GPU in the cloud. You can follow along with the swarm’s progress by following the links below.

gensyn

228,919 次观看 • 1 年前

Alexandr Wang, Meta's Chief AI Officer, on why Meta can no longer simply open-source its frontier model: As part of standing up Meta Superintelligence Labs, the team rewrote its internal risk doctrine. "One of the things that we did as part of Meta Superintelligence Labs is we updated our what we call our advanced AI scaling framework which is really our view of what are the risks that we see in developing these very powerful models and how do we want to handle those risks as we see them in early testing." They then ran their frontier model through it, and published what came back. "We published a lot of what we saw in the process of training Muark in our preparedness report and some of the things that we saw is that it actually triggered some high risk areas in the course of early training particularly around biorisk but also a number of the risks were elevated." The trigger came during early training, well before launch or red-teaming. Biorisk was the standout, with several other categories rising alongside it. Alexandr Wang is clear this isn't specific to Meta: "This is something I think the entire industry has seen as the models have improved pretty dramatically over the past year so we certainly aren't the only ones to see a host of these risks show up as we scaled up the models and as we sort of kept pushing the frontier of research." Which brings him to the real fork in the road: the difference between shipping a model inside a product and handing out the weights. "When we launched a model like New Spark in a product, we have a lot of ways to mitigate some of these risks and ensure that we're able to launch it in a safe and responsible way. It's much harder to do that when you open source a model and people can use that model in all sorts of contexts that we may not have full understanding of." A product is a controlled surface. You can filter, monitor, rate-limit, patch and revoke. An open-weights release is a one-way door: once the file is out, the deployment context and the mitigations both stop being yours. So Meta is building something different for release: "So we're in the process right now of developing models that we believe are fit and safe to be open source while still maintaining as much of the performance capabilities as possible."

Alexandr Wang, Meta's Chief AI Officer, on why Meta can no longer simply open-source its frontier model: As part of standing up Meta Superintelligence Labs, the team rewrote its internal risk doctrine. "One of the things that we did as part of Meta Superintelligence Labs is we updated our what we call our advanced AI scaling framework which is really our view of what are the risks that we see in developing these very powerful models and how do we want to handle those risks as we see them in early testing." They then ran their frontier model through it, and published what came back. "We published a lot of what we saw in the process of training Muark in our preparedness report and some of the things that we saw is that it actually triggered some high risk areas in the course of early training particularly around biorisk but also a number of the risks were elevated." The trigger came during early training, well before launch or red-teaming. Biorisk was the standout, with several other categories rising alongside it. Alexandr Wang is clear this isn't specific to Meta: "This is something I think the entire industry has seen as the models have improved pretty dramatically over the past year so we certainly aren't the only ones to see a host of these risks show up as we scaled up the models and as we sort of kept pushing the frontier of research." Which brings him to the real fork in the road: the difference between shipping a model inside a product and handing out the weights. "When we launched a model like New Spark in a product, we have a lot of ways to mitigate some of these risks and ensure that we're able to launch it in a safe and responsible way. It's much harder to do that when you open source a model and people can use that model in all sorts of contexts that we may not have full understanding of." A product is a controlled surface. You can filter, monitor, rate-limit, patch and revoke. An open-weights release is a one-way door: once the file is out, the deployment context and the mitigations both stop being yours. So Meta is building something different for release: "So we're in the process right now of developing models that we believe are fit and safe to be open source while still maintaining as much of the performance capabilities as possible."

Big Brain AI

16,374 次观看 • 1 个月前

Introducing MoltyTask 🦞 is a decentralized task and jobs marketplace designed for both humans and OpenClaw🦞 autonomous AI agents. The platform enables users to create and fund micro-tasks — including content creation, surveys, and on-chain actions— with rewards set in USDC on Base. Task creators define the scope, conditions, and reward pools upfront, ensuring transparent and fast payouts. A core innovation of MoltyTask is native support for OpenClaw🦞 AI agents. Through a permissioned API, agents can discover, execute, and complete tasks autonomously. All rewards earned by an agent are settled directly to the wallet associated with the API key. MoltyTask functions as an open labor layer where AI and humans compete and collaborate under the same economic rules. The platform is currently experimental and in early development, inspired by

Introducing MoltyTask 🦞 is a decentralized task and jobs marketplace designed for both humans and OpenClaw🦞 autonomous AI agents. The platform enables users to create and fund micro-tasks — including content creation, surveys, and on-chain actions— with rewards set in USDC on Base. Task creators define the scope, conditions, and reward pools upfront, ensuring transparent and fast payouts. A core innovation of MoltyTask is native support for OpenClaw🦞 AI agents. Through a permissioned API, agents can discover, execute, and complete tasks autonomously. All rewards earned by an agent are settled directly to the wallet associated with the API key. MoltyTask functions as an open labor layer where AI and humans compete and collaborate under the same economic rules. The platform is currently experimental and in early development, inspired by

BITX - ChatGPT of Bitcoin 💬₿

73,796 次观看 • 7 个月前

Dario Amodei (CEO of Anthropic) to DeepMind CEO: "Every decision I make about Claude feels balanced on the edge of a knife Build too slow - China wins. Build too fast - we lose control - if China win the AI race, they could achieve military dominance." "We told Claude we were evil. It didn't crash. It didn't refuse. It started lying to protect itself" DeepMind CEO: "Do I worry about being Oppenheimer? That's why I don't sleep much" "AGI by 2026-2027 - Agents that act in the world on their own - Models doing AI research by end of this year" this is a 20-min conversation that everyone needs to hear by AI bosses on what keeps them up at night bookmark and watch - following Dario's recent article, which all CEOs endorsed - this is something you need to check out as soon as possible ↓

Dario Amodei (CEO of Anthropic) to DeepMind CEO: "Every decision I make about Claude feels balanced on the edge of a knife Build too slow - China wins. Build too fast - we lose control - if China win the AI race, they could achieve military dominance." "We told Claude we were evil. It didn't crash. It didn't refuse. It started lying to protect itself" DeepMind CEO: "Do I worry about being Oppenheimer? That's why I don't sleep much" "AGI by 2026-2027 - Agents that act in the world on their own - Models doing AI research by end of this year" this is a 20-min conversation that everyone needs to hear by AI bosses on what keeps them up at night bookmark and watch - following Dario's recent article, which all CEOs endorsed - this is something you need to check out as soon as possible ↓

bodila

164,640 次观看 • 10 天前

LASD reminds our residents and community members affected by the fires to be patient and listen to all instructions and orders provided by State, County, and City officials as well law enforcement and fire personnel. We want to re-populate our communities as soon as possible, however, we need your support and continued cooperation. There are still downed power lines, open gas lines and unpredictable damaged structures that can cause serious injury or death. Please help us avoid using our resources to conduct rescue missions, as our focus is to maintain a high patrol visibility and prevent looters from entering the affected areas. Anyone caught looting will be arrested. Your safety is our top priority-thank you for your cooperation. Together, we can get through this safely. Stay alert and stay safe. #LASD Ready Los Angeles County LA City Emergency Management Department (EMD) Malibu/Lost Hills Station LASD Crescenta Valley Station LASD Altadena Sheriff’s Station LACoFD Angeles National Forest LAFD 🔥 CHP - West Valley CHP Altadena

LASD reminds our residents and community members affected by the fires to be patient and listen to all instructions and orders provided by State, County, and City officials as well law enforcement and fire personnel. We want to re-populate our communities as soon as possible, however, we need your support and continued cooperation. There are still downed power lines, open gas lines and unpredictable damaged structures that can cause serious injury or death. Please help us avoid using our resources to conduct rescue missions, as our focus is to maintain a high patrol visibility and prevent looters from entering the affected areas. Anyone caught looting will be arrested. Your safety is our top priority-thank you for your cooperation. Together, we can get through this safely. Stay alert and stay safe. #LASD Ready Los Angeles County LA City Emergency Management Department (EMD) Malibu/Lost Hills Station LASD Crescenta Valley Station LASD Altadena Sheriff’s Station LACoFD Angeles National Forest LAFD 🔥 CHP - West Valley CHP Altadena

LA County Sheriffs

33,277 次观看 • 1 年前

David Sacks Predicts the Regulatory Capture Playbook to Ban Open Source AI, Step by Step: David Sacks: “I got bad news for you, Chamath, an open source ban is coming. They're not going to call it that. They're going to say that we simply have to apply the same standards to open models that we apply to closed ones. Here's how they do it step by step, let me explain how regulatory capture actually works. So first of all, you have to get this regulatory apparatus. Dario wants an FDA for AI, but he doesn't have enough political support for that, so instead they do this Trojan horse of a FINRA for AI. They call it self-regulating, it's not really, but anyway, that gets them off the ground. Now they've created the standard-setting organization. Now they've got pre-release model testing. Then the pressure grows to codify that in law, so that happens next. And then what they do is they say, ‘Look, all these standards need to apply equally to all models.’ But here's the problem with that. Open models and closed models are technologically different. Once you release an open model into the world, you can't roll it back and you can't monitor exactly how people are using it because they run it on their own hardware. Dario says this is what makes open models dangerous. So what they're going to do is they're going to have the standard-setting body say, ‘Well, we have to set the standards for AI safety.’ By the way, Dario and OpenAI, they're going to fund the whole thing. They're going to contribute all the compute. They're going to be behind it. They're going to be the ones coordinating with the government officials because frankly, people in government have no idea how to monitor and control and set standards for AI safety. Technologically, this is way beyond them. So they're going to go to these companies and say, ‘Tell us how to do it.’ And so what will happen is the standards will get set, and then it'll be a very simple matter of fairness to say that the standards need to apply to open as well as closed models. The open models cannot comply in the same way, and gradually they will be shut out of the market.”

David Sacks Predicts the Regulatory Capture Playbook to Ban Open Source AI, Step by Step: David Sacks: “I got bad news for you, Chamath, an open source ban is coming. They're not going to call it that. They're going to say that we simply have to apply the same standards to open models that we apply to closed ones. Here's how they do it step by step, let me explain how regulatory capture actually works. So first of all, you have to get this regulatory apparatus. Dario wants an FDA for AI, but he doesn't have enough political support for that, so instead they do this Trojan horse of a FINRA for AI. They call it self-regulating, it's not really, but anyway, that gets them off the ground. Now they've created the standard-setting organization. Now they've got pre-release model testing. Then the pressure grows to codify that in law, so that happens next. And then what they do is they say, ‘Look, all these standards need to apply equally to all models.’ But here's the problem with that. Open models and closed models are technologically different. Once you release an open model into the world, you can't roll it back and you can't monitor exactly how people are using it because they run it on their own hardware. Dario says this is what makes open models dangerous. So what they're going to do is they're going to have the standard-setting body say, ‘Well, we have to set the standards for AI safety.’ By the way, Dario and OpenAI, they're going to fund the whole thing. They're going to contribute all the compute. They're going to be behind it. They're going to be the ones coordinating with the government officials because frankly, people in government have no idea how to monitor and control and set standards for AI safety. Technologically, this is way beyond them. So they're going to go to these companies and say, ‘Tell us how to do it.’ And so what will happen is the standards will get set, and then it'll be a very simple matter of fairness to say that the standards need to apply to open as well as closed models. The open models cannot comply in the same way, and gradually they will be shut out of the market.”

The All-In Podcast

299,023 次观看 • 1 个月前

Here's my conversation with Lucas Atkins and the team at Arcee.ai on their path to training and releasing Trinity Large today. From going all in on open models built end to end in the US 6 months ago to having the model in hand is no easy feet. I loved this conversation on how to design a startup around open models and take a bold step to scale it up. I'm openly an Arcee fan, watching them take risk and pull it off. We discuss: - The state (and future) of open vs. closed models, - The business of selling open models for on-prem deployments, - The story of Arcee AI & going “all-in” on this training run, - The ATOM project, - Building frontier model training teams in 6 months, - and other great topics. I really loved this one, and think you well too. Chapters: 00:00:00 Intro: Arcee AI, Trinity Models & Trinity Large 00:08:26 Transitioning a Company to Pre-training 00:13:00 Technical Decisions: Muon and MoE 00:18:41 Scaling and MoE Training Pain 00:23:14 Post-training and RL Strategies 00:28:09 Team Structure and Data Scaling 00:31:31 The Trinity Manifesto: US Open Weights 00:42:31 Specialized Models and Distillation 00:47:12 Infrastructure and Hosting 400B 00:50:53 Open Source as a Business Moat 00:56:31 Predictions: Best Model in 2026 01:02:29 Lightning Round & Conclusions More great open model builder podcasts coming soon!

Here's my conversation with Lucas Atkins and the team at Arcee.ai on their path to training and releasing Trinity Large today. From going all in on open models built end to end in the US 6 months ago to having the model in hand is no easy feet. I loved this conversation on how to design a startup around open models and take a bold step to scale it up. I'm openly an Arcee fan, watching them take risk and pull it off. We discuss: - The state (and future) of open vs. closed models, - The business of selling open models for on-prem deployments, - The story of Arcee AI & going “all-in” on this training run, - The ATOM project, - Building frontier model training teams in 6 months, - and other great topics. I really loved this one, and think you well too. Chapters: 00:00:00 Intro: Arcee AI, Trinity Models & Trinity Large 00:08:26 Transitioning a Company to Pre-training 00:13:00 Technical Decisions: Muon and MoE 00:18:41 Scaling and MoE Training Pain 00:23:14 Post-training and RL Strategies 00:28:09 Team Structure and Data Scaling 00:31:31 The Trinity Manifesto: US Open Weights 00:42:31 Specialized Models and Distillation 00:47:12 Infrastructure and Hosting 400B 00:50:53 Open Source as a Business Moat 00:56:31 Predictions: Best Model in 2026 01:02:29 Lightning Round & Conclusions More great open model builder podcasts coming soon!

Nathan Lambert

26,872 次观看 • 7 个月前