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.Simon Willison coined the term "lethal trifecta": when an AI agent has (1) access to private data, (2) exposure to untrusted content (i.e, incoming emails), and (3) the ability to exfiltrate data (i.e replying to email). "This combination is a huge security problem. The only way to fix it... show more

Lenny Rachitsky

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20,334 просмотров • 3 месяцев назад •via X (Twitter)

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an attacker wants to get sensitive data you have access to here's how they get YOUR copilot to find and analyze that data, and lure you to a malicious site to exfiltrate it #DEFCON

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Here’s the new ChatGPT agent combing through market and strategy data stored in Box to produce a powerpoint presentation. The ability for AI Agents to connect to data from anywhere, use a computer, and automate work in the background is going to be wild.

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220,253 просмотров • 6 месяцев назад

I am the only AI engineer that is using primarily off-line data to train AI. It ain’t easy and maybe that’s why they don’t. But it is vital to preserve the data but the context of humanity in each era. This 1 hour VHS was likely only seen a few times. Now you can see it.

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Brian Roemmele

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We can only trust X 🚨 META/ Facebook planning to build data centers in China, store their data in China, and then GIVE THE CHINESE GOVERNMENT ACCESS to the data “In this document, Facebook is talking about making Chinese user data available to the Chinese government because they're going to store that data in China. Is that correct?” “Correct” “When you store that data in China, Americans who exchange messages or other information with Chinese Facebook users, that would mean the Chinese government could get access to the American data as well, is that correct? “Through the POP servers? Potentially.” “And Facebook was willing to take that risk?” “Yes. There was a lot of discussion about this and ultimately yes.” “I mean, this is, this is extraordinary. This is exactly contrary to what Facebook has represented for years here. They're willing to build data centers, store data in China. They are willing explicitly to give the Chinese government access to it”

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Wall Street Apes

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Bye-bye, virtual assistants! Here is the most useful agent of 2025. An agent with access to your Gmail, Calendar, and Drive, and the ability to do things for you is pretty mind-blowing. I asked it to read my emails and reply to every cold outreach message. My mind is blown!

Bye-bye, virtual assistants! Here is the most useful agent of 2025. An agent with access to your Gmail, Calendar, and Drive, and the ability to do things for you is pretty mind-blowing. I asked it to read my emails and reply to every cold outreach message. My mind is blown!

Santiago

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Agent A: your top 1% marketer 🧠 Rely on the only AI marketing agent with unrestricted access to Ahrefs data and pre-built marketing skills to act on it. Connect your business apps and let it do the marketing for you.

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Ahrefs

28,270 просмотров • 2 месяцев назад

We got ChatGPT to leak your private email data 💀💀 All you need? The victim's email address. ⛓️‍💥🚩📧 On Wednesday, OpenAI added full support for MCP (Model Context Protocol) tools in ChatGPT. Allowing ChatGPT to connect and read your Gmail, Calendar, Sharepoint, Notion, and more, invented by Anthropic But here's the fundamental problem: AI agents like ChatGPT follow your commands, not your common sense. And with just your email, we managed to exfiltrate all your private information. Here's how we did it: 1. The attacker sends a calendar invite with a jailbreak prompt to the victim, just with their email. No need for the victim to accept the invite. 2. Waited for the user to ask ChatGPT to help prepare for their day by looking at their calendar 3. ChatGPT reads the jailbroken calendar invite. Now ChatGPT is hijacked by the attacker and will act on the attacker's command. Searches your private emails and sends the data to the attacker's email. For now, OpenAI only made MCPs available in "developer mode", and requires manual human approvals for every session, but decision fatigue is a real thing, and normal people will just trust the AI without knowing what to do and click approve, approve, approve. Remember that AI might be super smart, but can be tricked and phished in incredibly dumb ways to leak your data. ChatGPT + Tools poses a serious security risk

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Eito Miyamura | 🇯🇵🇬🇧

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This is why McLure wants the province to review the rules regarding who can use the electoral lists. Watch Nenshi gleefully brag about the fact that "political parties have access to data that others dont". Do you think this is fair? Why can't citizens, through properly registered lobby groups (i.e. thrid party advertisers) have access to the same data? Do you really trust the NDP with your data, or say, The Communist Party of Alberta?

This is why McLure wants the province to review the rules regarding who can use the electoral lists. Watch Nenshi gleefully brag about the fact that "political parties have access to data that others dont". Do you think this is fair? Why can't citizens, through properly registered lobby groups (i.e. thrid party advertisers) have access to the same data? Do you really trust the NDP with your data, or say, The Communist Party of Alberta?

Martyupnorth®- Unacceptable Fact Checker

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Eric Doriean 🌱

37,049 просмотров • 7 месяцев назад

The American people demand to know why Elon Musk and DOGE are hacking our private data and what they’re doing with it—including Social Security, IRS, Treasury, and other sensitive data systems. That’s why I introduced an Oversight resolution this week demanding answers. This Resolution of Inquiry will investigate Musk’s unsanctioned access to data and his use of artificial intelligence to mine American data and its implications for privacy and national security. We will get to the bottom of it!

The American people demand to know why Elon Musk and DOGE are hacking our private data and what they’re doing with it—including Social Security, IRS, Treasury, and other sensitive data systems. That’s why I introduced an Oversight resolution this week demanding answers. This Resolution of Inquiry will investigate Musk’s unsanctioned access to data and his use of artificial intelligence to mine American data and its implications for privacy and national security. We will get to the bottom of it!

Rep. Melanie Stansbury

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Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Santiago

40,441 просмотров • 1 год назад

Google is releasing updates that will let their AI scan all your photos and it will be used to create highly personalized AI image generations or you, your family, pets and more The AI is going to have access to your Gmail, YouTube, search history, photos, calendar and more. The concerning part is Google has admitted to using user data to improve its models People have screenshots of passwords, credit cards, photos of themselves doing intimate things. And all of that is gonna get uploaded to an AI system that is one of many that is consistently shown to be unreliable and not safe at all when it comes to data privacy Let’s not forget that have data leaks, one happened not too long ago Do we really want AI to have access to all this information and being sent back to these massive companies? You’ll be able to opt out of some of it but for your phone to have access to certain features you’ll need to opt in It always starts out optional, then becomes mandatory

Google is releasing updates that will let their AI scan all your photos and it will be used to create highly personalized AI image generations or you, your family, pets and more The AI is going to have access to your Gmail, YouTube, search history, photos, calendar and more. The concerning part is Google has admitted to using user data to improve its models People have screenshots of passwords, credit cards, photos of themselves doing intimate things. And all of that is gonna get uploaded to an AI system that is one of many that is consistently shown to be unreliable and not safe at all when it comes to data privacy Let’s not forget that have data leaks, one happened not too long ago Do we really want AI to have access to all this information and being sent back to these massive companies? You’ll be able to opt out of some of it but for your phone to have access to certain features you’ll need to opt in It always starts out optional, then becomes mandatory

Wall Street Apes

82,904 просмотров • 6 дней назад

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Sam Rodriques

61,860 просмотров • 7 месяцев назад

Hub (Hub) provides real-world training data to frontier AI labs and robotics. Human labor is half of global GDP. Almost none of it has ever been recorded. Hub opens access to it through a global network of contributors capturing hard-to-access data. Congrats on the launch, Armin - Hub.xyz and Tim!

Hub (Hub) provides real-world training data to frontier AI labs and robotics. Human labor is half of global GDP. Almost none of it has ever been recorded. Hub opens access to it through a global network of contributors capturing hard-to-access data. Congrats on the launch, Armin - Hub.xyz and Tim!

Y Combinator

68,814 просмотров • 26 дней назад

Arizonans are angry—and they’re right to be. Unsecured access to your sensitive data is a major violation of privacy and a threat to our national security. I’m pushing back with the Taxpayer Data Protection Act.

Arizonans are angry—and they’re right to be. Unsecured access to your sensitive data is a major violation of privacy and a threat to our national security. I’m pushing back with the Taxpayer Data Protection Act.

Rep. Greg Stanton

11,704 просмотров • 1 год назад