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EMM_386on Anthropic’s paper smells like bullshit

The prompts aren't the key to the attack, though. They were able to get around guardrails with task decomposition.

There is no way for the AI system to verify whether you are white hat or black hat when you are doing pen-testing if the only task is to pen-test. Since this is not part of a "broader attack" (in the context), there is no "threat".

I don't see how this can be avoided, given that there are legitime uses to every step of this in creating defenses to novel attacks.

Yes, all of this can be done with code and humans as well - but it is the scale and the speed that becomes problematic. It can adjust in real-time to individual targets and does not need as much human intervention / tailoring.

Is this obvious? Yes - but it seems they are trying to raise awareness of an actual use of this in the wild and get people discussing it.

EMM_386on Anthropic’s paper smells like bullshit

Anthropic is not a security vendor.

They're an AI research company that detected misuse of their own product. This is like "Microsoft detected people using Excel macros for malware delivery" not "Mandiant publishes APT28 threat intelligence". They aren't trying to help SOCs detect this specific campaign. It's warning an entire industry about a new attack modality.

What would the IoCs even be? "Malicious Claude Code API keys"?

The intended audience is more like - AI safety researchers, policy makers, other AI companies, the broader security community understanding capability shifts, etc.

It seems the author pattern-matched "threat intelligence report" and was bothered that it didn't fit their narrow template.

EMM_386on Anthropic Rides an Artificial Wave

This isn't what I got from the Anthropic article.

It's the fact that somebody like NSO Group could create a Pegasys in much faster time without humans in the loop.

Write down everything your most brilliant hacker minds know - put it in some documents, feed it do the AI with the workflow you usually do and let 50 AI agents do them all at the same time.

Put the information in the documents about how you thought to come up with those exploits, how you tested them, how you explored the possibility. Tell the AI to do that too.

We're talking about speed and scale, not some dismissive "script kiddies were doing this in the 90s".

Yes, we were. But not at this scale - and this is just the early days of this sort of thing.

EMM_386on AI World Clocks

You can absolutely do this. In fact, with Claude Anthropic encourages you to send it screenshots. It works very well if you aren't expecting pixel-perfection.

YMMV with other models but Sonnet 4.5 is good with things like this - writing the code, "seeing" the output and then iterating on it.

EMM_386on Anthropic invests $50B in US AI infrastructure

How do you think computing companies work?

They buy hardware, replace it as the years go on, and continue doing business.

The investment isn't just in raw compute - they have to build buildings, pay staff, and other things. For the hardware and software - they just keep pace as all the other computing companies have to.

EMM_386on The lazy Git UI you didn't know you need

VS Code is free, cross-platform, many people already use it, and has a very good GUI interface for git.

It can easily do all the common workflows.

I mainly use the CLI but if I already have a project open in VS Code I'll just do it in the GUI because it's actually faster in many cases and sometimes a bit more intuitive.

EMM_386on Kimi K2 Thinking, a SOTA open-source trillion-parameter reasoning model

I think one of the issues is that LLMs can't have a "Go" model and an "HTML model". I mean, they can but what would that contain? It's not the language-specific features that make models large.

When models work on your code base, they do not "see" things like this, which is why they can go through an entire code base with variable names they have never seen before, function signatures they have never seen before, and directory structures that have never seen before and not have a problem.

You need that "this is a variable, which is being passed to a function which recursively does ..." part. This is not something language specific, it's the high level understanding of how languages and systems operate. A variable is a variable whether in JavaScript or C++ and LLMs can "see" it as such. The details are different but it's that layer of "this is a software interface", "this is a function pointer" is outside of the "Go" or "Python" or "C#" model.

I don't know how large the main model would have to be vs. the specialized models in order to pick this dynamic up.

EMM_386on The 512KB Club

You don't even need a framework for a SPA.

I have a SPA that is just vanilla web components and is clean, small, fast and tidy. No outside frameworks, just my own small amount of transpiled TypeScript.

I prefer to write them that way because it meets my needs for the particular site and is very responsive. But I've also done PHP, ASP.Net, Rails and other server-side work for many years. Best tool for the job, and sometimes they are very different.

EMM_386on Andrej Karpathy – It will take a decade to work through the issues with agents

> How large is a lion?

Ask a blind person that question - they can answer it.

Too many people think you need to "see" as in human sight to understand things like this. You obviously don't. The massive training data these models ingest is more than sufficient to answer this question - and not just by looking up "dimensions of a lion" in the high-dimensional space.

The patterns in that space are what generates the concept of what a lion is. You don't need to physically see a lion to know those things.

EMM_386on Surveillance data challenges what we thought we knew about location tracking