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Application security consultant interested in misinformation, film, Wikipedia. Curated Whats the Harm website and other skeptic/science stuff over the years. #StarTrek #Jazz
Twitter@krelnik
"Anthropic is helping the US National Security Agency deploy its powerful Mythos AI model for offensive cyber operations [... Anthropic] had installed about half a dozen staff within the NSA as so-called forward-deployed engineers to guide the use of the technology and customise models for specific applications“
https://www.ft.com/content/d02d91b3-2636-454e-9442-dc7e69f51815?syn-25a6b1a6=1
US National Security Agency using Anthropic’s Mythos for cyber attacks

Arrangement comes as AI lab is locked in legal battle with Pentagon over Claude model

Financial Times
This explains a lot about what I’ve been experiencing with combatting scrapers. https://blog.includesecurity.com/2026/06/the-smart-tv-in-your-livingroom-is-a-node-in-the-aiscraping-economy/
The Smart TV in Your LivingRoom Is a Node in the AIScraping Economy - Include Security Research Blog

In this post we look under the hood of BrightData's SDK and how it turns ordinary consumer TVs into exit nodes of an enormous commercial, residential proxy network leveraged by the AI industry to scrape web data and train language learning models.

Include Security Research Blog

@mhoye that company has cultivated a work environment that gives people license to build repugnant contraptions.

https://www.infosecurity-magazine.com/news/researchers-linkedin-intro-is-a-man-in-the-middle/

I do not believe LinkedIn/Microsoft can be fixed.

Researchers: LinkedIn Intro is a Man-in-the-Middle Attack

LinkedIn has released a new product called Intro, which shows users' LinkedIn profiles from inside the native iPhone mail client. Members can, at-a-glance, see the profile picture of the person who’s emailing, learn more about their background, and connect on LinkedIn. It sounds like another step in the march to hyper-connected convenience, but at least one research group has raised security concerns over the functionality.

Infosecurity Magazine

LLM hallucinations in the wild: Large-scale evidence from non-existent citations

Zhenyue Zhao, Yihe Wang, Toby Stuart, Mathijs De Vaan, Paul Ginsparg, Yian Yin

"Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain poorly understood. Here we leverage a uniquely verifiable object - scientific citations - to audit 111 million references across 2.5 million papers in arXiv, bioRxiv, SSRN, and PubMed Central. We find a sharp rise in non-existent references following widespread LLM adoption, with a conservative estimate of 146,932 hallucinated citations in 2025 alone. These errors are diffusely embedded across many papers but especially pronounced in fields with rapid AI uptake, in manuscripts with linguistic signatures of AI-assisted writing, and among small and early-career author teams. At the same time, hallucinated references disproportionately assign credit to already prominent and male scholars, suggesting that LLM-generated errors may reinforce existing inequities in scientific recognition. Preprint moderation and journal publication processes capture only a fraction of these errors, suggesting that the spread of hallucinated content has outpaced existing safeguards. Together, these findings demonstrate that LLM hallucinations are infiltrating knowledge production at scale, threatening both the reliability and equity of future scientific discovery as human and AI systems draw on the existing literature."

https://arxiv.org/abs/2605.07723

#AI #GenerativeAI #LLMs #Hallucinations #AcademicPublishing #Science

LLM hallucinations in the wild: Large-scale evidence from non-existent citations

Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain poorly understood. Here we leverage a uniquely verifiable object - scientific citations - to audit 111 million references across 2.5 million papers in arXiv, bioRxiv, SSRN, and PubMed Central. We find a sharp rise in non-existent references following widespread LLM adoption, with a conservative estimate of 146,932 hallucinated citations in 2025 alone. These errors are diffusely embedded across many papers but especially pronounced in fields with rapid AI uptake, in manuscripts with linguistic signatures of AI-assisted writing, and among small and early-career author teams. At the same time, hallucinated references disproportionately assign credit to already prominent and male scholars, suggesting that LLM-generated errors may reinforce existing inequities in scientific recognition. Preprint moderation and journal publication processes capture only a fraction of these errors, suggesting that the spread of hallucinated content has outpaced existing safeguards. Together, these findings demonstrate that LLM hallucinations are infiltrating knowledge production at scale, threatening both the reliability and equity of future scientific discovery as human and AI systems draw on the existing literature.

arXiv.org
@tully 🎨 🖼️
@krelnik They also could have found you and recently looked at your profile. That often spawns the "New Friend Recommendation" feature.
Wow I know #Meta et.al. do some creepy stuff to find out about us. (Google "shadow profiles" if you doubt). But their Instagram app just did something truly weird. It just recommended to me a person who was a childhood friend OVER 50 YEARS AGO. I haven't lived there in 51 years. He no longer lives there either. He currently lives 5 hours by car away from me in a different state. We have had no contact since we were kids. We have NO friends in common. We have no common interests that I can see from his Instagram or Facebook. (In fact, based on some media accounts he follows I doubt we would be super friendly as adults). I literally only knew him because he lived like 8 houses away when I was a child and we went to the same neighborhood church. We just played with Tonka trucks and G.I. Joes together. Kind of dumbfounded at this. Is Meta buying former residence locations from data brokers and matching up people who lived in the same neighborhood as kids? A bit of a reach and really demonstrates how creepy their surveillance infrastructure is. #SurveillanceCapitalism
Lo and behold

“Each elimination looks rational in isolation. The second-order effects arrive six months later, and by then nobody connects the locust swarm to the dead sparrows.”

https://leehanchung.github.io/blogs/2026/04/05/the-ai-great-leap-forward/

The AI Great Leap Forward

In 1958, Mao ordered every village to produce steel. The steel was useless. The crops rotted. Today's top-down AI mandates are producing the same pattern: ba...

Han, Not Solo

This year's April Fool's Day science papers are coming online.

First, from our favorite astrophysicist @sundogplanets

1. Cow-culation: Reentry Impact Risk to Livestock in the Satellite Megaconstellation Era

... is launching more satellites into LEO every year.
This could intersect with NZ’s famously large population of livestock. We predict this will be an udder disaster for any cows that are hit, as they are squishy and moo-ve much more slowly than space debris.

https://arxiv.org/abs/2603.29324
1/n