#ESETresearch has mapped the labyrinth of #AsyncRAT forks, identifying the most prevalent versions of this open-source malware. While some variants are mere curiosities, others pose a more tenacious threat. https://www.welivesecurity.com/en/eset-research/unmasking-asyncrat-navigating-labyrinth-forks/
AsyncRAT comes with the typical RAT functionalities, including keylogging, screen capturing, and credential theft. Other threat actors have developed a multitude of variants based on its source code.
Our analysis revealed the most widely used and deployed forks of AsyncRAT, with the most prevalent among them being #DcRat.
Although DcRat holds a smaller share compared to AsyncRAT, it offers notable improvements. These include advanced evasion techniques, and the use of an open-source library for more efficient binary data serialization.
AsyncRAT forks often include prank-style plugins, such as for opening and closing the CD tray and turning off the monitor. Spoof versions dubbed SantaRAT and BoratRAT have also emerged – mostly intended as jokes.
AsyncRAT and its variants demonstrate how quickly and creatively threat actors can adapt open-source code – especially with the assistance of #LLMs. This underscores the importance of proactive detection and effective analysis of emerging threats.
IoCs available on our GitHub: https://github.com/eset/malware-ioc/tree/master/

Because search engines (Google in particular) have absolutely failed me, I'm gonna crowd source this:

I'm looking for long-form blog posts on the state of #AI today. I don't mind if they get a bit technically, I'm just trying to get a deeper understanding of what #llms can do, how they work, their limitations and potential. I feel like most of what I've been exposed to is either overly optimistic takes (which I generally find off-putting) and the pessimistic takes which appeal to my cynicism (unfortunately). But I'm trying to be more open-minded, now.

I've seen a few talks on YouTube, one from Andrej Karpathy on his channel, and another one by Jodie Burchell on GOTO conferences which I think were pretty good. I'm just tired of being a non-believer who can't properly explain from a technical perspective why I don't believe other than the fact that I've tried to use LLMs for actual complex tasks and even the almighty Claude seems to crumble under real pressure

#askfedi #generativeAI #technology #blog

…the term hallucinations is subtly misleading. It suggests that the bad behavior is an aberration, a bug, when it’s actually a feature of the probabilistic pattern-matching mechanics of neural networks.
—Karen Hao, Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
#ai#llms#llm #hallucinations

in the context of writing a talk for an upcoming pre-conference workshop at #cogsci2025 I’m looking for examples of researchers who explicitly maintain that #LLMs do not or cannot “reason”….

all suggestions welcome!

”We’re focused on how LLMs hallucinate, but maybe we should pay more attention to the ways we are being encouraged to hallucinate about them. The idea of “artificial general intelligence” or “conscious machines” is more an advertising strategy than a near term engineering reality.”
—Matthew David Segall, A Biophilosophical Dialogue
https://footnotes2plato.substack.com/p/a-biophilosophical-dialogue
#ai#llm#llms

My personal "are #LLMs progressing towards #AGI" benchmark is when any of them can successfully count the words in a passage of text (without a giant bag of prompt hacks)

A quite nuanced expert investigation into possible #activist applications of #AI ☺️🤖 https://www.juniperbevensee.com/musings/does-ai-suck #LLMs
Personally I am in the anarchic "hate it" camp but even so, https://bsky.app/profile/juniperbevensee.bsky.social illuminates a lot about how bias and censorship are encoded into #ChatGPT & other commercial tools.

are infants initially learning something like a foundation model?

“we propse that in the helpless period, infants are learning their own foundation models, developing the representations that will facilitate the subsequent rapid development of complex tasks such as walking and talking. As in machines, the benefit will be better and more efficient generalisation to tasks learned later in life. This proposal aligns with cognitive theories that emphasise the value of identifying patterns in perceptual inputs as a foundation for concept learning..”

#LLMs @cogsci

https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(24)00114-1

“We collected 4.7 million triplet judgements from LLMs and multimodal #LLMs to derive low-dimensional embeddings that capture the similarity structure of 1,854 natural objects. The resulting 66-dimensional embeddings were stable, predictive and exhibited semantic clustering similar to human mental representations. Remarkably, the dimensions underlying these embeddings were interpretable, suggesting that LLMs and multimodal LLMs develop human-like conceptual representations of objects. Further analysis showed strong alignment between model embeddings and neural activity patterns in brain regions such as the extrastriate body area, parahippocampal place area, retrosplenial cortex and fusiform face area. “

https://www.nature.com/articles/s42256-025-01049-z

really interesting blog post by Konrad Kording on co-adaptation and how that might limit the extent to which the development of AI systems can benefit directly from expanding knowledge about brains @cogsci #LLMs#AI @kordinglab

https://kording.substack.com/p/lions-and-dolphins-can-not-make-babies

"Let’s not forget that the industry building AI Assistants has already made billions of dollars honing the targeted advertising business model. They built their empires by drawing our attention, collecting our data, inferring our interests, and selling access to us.

AI Assistants supercharge this problem. First because they access and process incredibly intimate information, and second because the computing power they require to handle certain tasks is likely too immense for a personal device. This means that very personal data, including data about other people that exists on your phone, might leave your device to be processed on their servers. This opens the door to reuse and misuse. If you want your Assistant to work seemlessly for you across all your devices, then it’s also likely companies will solve that issue by offering cloud-enabled synchronisation, or more likely, cloud processing.

Once data has left your device, it’s incredibly hard to get companies to be clear about where it ends up and what it will be used for. The companies may use your data to train their systems, and could allow their staff and ‘trusted service providers’ to access your data for reasons like to improve model performance. It’s unlikely what you had all of this in mind when you asked your Assistant a simple question.

This is why it’s so important that we demand that our data be processed on our devices as much as possible, and used only for limited and specific purposes we are aware of, and have consented to. Companies must be provide clear and continuous information about where queries are processed (locally or in the cloud) and what data has been shared for that to happen, and what will happen to that data next."

https://privacyinternational.org/news-analysis/5591/are-ai-assistants-built-us-or-exploit-us-and-other-questions-ai-industry

#AI#GenerativeAI #LLMs #Chatbots#AIAssistants#Privacy#AdTech#DataProtection#AdTargeting

"Asking scientists to identify a paradigm shift, especially in real time, can be tricky. After all, truly ground-shifting updates in knowledge may take decades to unfold. But you don’t necessarily have to invoke the P-word to acknowledge that one field in particular — natural language processing, or NLP — has changed. A lot.

The goal of natural language processing is right there on the tin: making the unruliness of human language (the “natural” part) tractable by computers (the “processing” part). A blend of engineering and science that dates back to the 1940s, NLP gave Stephen Hawking a voice, Siri a brain and social media companies another way to target us with ads. It was also ground zero for the emergence of large language models — a technology that NLP helped to invent but whose explosive growth and transformative power still managed to take many people in the field entirely by surprise.

To put it another way: In 2019, Quanta reported on a then-groundbreaking NLP system called BERT without once using the phrase “large language model.” A mere five and a half years later, LLMs are everywhere, igniting discovery, disruption and debate in whatever scientific community they touch. But the one they touched first — for better, worse and everything in between — was natural language processing. What did that impact feel like to the people experiencing it firsthand?

Quanta interviewed 19 current and former NLP researchers to tell that story. From experts to students, tenured academics to startup founders, they describe a series of moments — dawning realizations, elated encounters and at least one “existential crisis” — that changed their world. And ours."

https://www.quantamagazine.org/when-chatgpt-broke-an-entire-field-an-oral-history-20250430/

#AI#GenerativeAI#ChatGPT#NLP#OralHistory #LLMs #Chatbots

✍️ : A blog post that I wanted to write since two years!

https://www.onceupondata.com/post/2025-01-05-philosophy-ai/

#AI#responsibleAI #LLMs#GenAI#AIethics #tech