7 ms·

AI models need a virtual machine

234 points by azhenley 1y ago

bobrx4 1y ago
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deleted 1y ago
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mehulashah 1y ago
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.
- csmpltn 1y ago
  This is the wrong take. There's a point beyond which LLMs are an overkill, where a simple script or a "classic" program can outdo the LLM across speed, accuracy, scalability, price and more. LLMs aren't supposed to solve "universal computing". They are another tool in the toolbox, and it's all about using the right tool for the problem.
  - mehulashah 1y ago
    I shared your opinion for a while. But, that’s not what’s happening. People are using them for everything. When they do, expectations are set. Vendors will adjust and so will the rest of the industry. It’s happening.
    - kylebenzle 1y ago
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    - baby_souffle 1y ago
      > People are using them for everything Let’s see if that continues to be the case after some time. On a long enough timeline, that 100 line python script that is deterministic is going to beat the non deterministic llm. They are a tool. They re not an omnitool.
      - Imustaskforhelp 1y ago
        If all you have is a hammer, everything looks like a screw. The problem is not the tool, the problem are the people selling the hype, the people accepting the hype as it is and this "everyone" person who I keep on listening about, I wonder why his takes are often times wrong but he doesn't accept them... People can be wrong, People are wrong in a lot of contexts, I don't think the world is efficient in this sense. We are emotional beings, sell us hype and we would accept it and run with it. Agree on how it is not an omnitool. Why are we going towards an inferior product (AI for VMS??) when this doesn't make sense when we already have superior product (I think) I guess somethings don't make sense and if everyone jumps in a well, most people would follow it (maybe including myself, if I didn't want to be contrarian in such sense, my knowledge is really limited regarding AI so all of the above statements can be wrong and hey I am wrong I usually am)
      - IX-103 1y ago
        They're pretty close to being an omnitool. But like any tool you need to know how to use it. Asking it to do some common task is probably silly when you should be asking it to write a program to do the common task (and add the program to its list of tools if it works).
    - kibwen 1y ago
      At the risk of being blunt, this comment reads like someone in the throes of religious euphoria. It makes no sense to call LLMs "the new universal computing machines". Please take a step back and reevaluate the media bubbles you're pickling your brain in.
      - mehulashah 1y ago
        Trust me. I’m usually the last to jump on a bandwagon. That said, this is not just my take, but the take of many others that I trust. Andrej Karpathy, Joseph Hellerstein, etc.
      - IX-103 1y ago
        There was a time when everybody did arithmetic by hand and "calculator" was a profession. There was a time when spreadsheets were made with ink and paper. There was a time when all programs were written in assembly. Now, most arithmetic is done by computers, spreadsheets are done by computers, and almost all assembly language is written by computers. People have moved on to higher level programming languages for communicating ideas to computers. Would it really be that surprising to learn that in the future people use natural language to speak with computers? The company I work for went through a big AI push about a month ago. Before then, almost no code was written by AI. Now, it's the majority of code. I'm not saying that AI coders will be able to replace people, because the AI honestly is just not as capable - if it were interviewing for my company, I would not hire it. But the thing is that I can go from design doc to prototype using less than a day and $10 of tokens. Sure I have to make corrections and rewrite some of the more fiddly bits, but it saves me a ton of typing. And LLMs are not limited to programming. Any computer-based task that you would allow an unpaid intern to complete for you would be a reasonable fit for AI.
    - greenavocado 1y ago
      We need fusion power plants as soon as possible because pretty soon it we will need to boil the oceans to write a Hello World in a hypothetical LLM VM, nevermind actually running the thing, which will require a Dyson Sphere at minimum
      - dingnuts 1y ago
        well Sam A said that is the plan
    - Swizec 1y ago
      > People are using them [LLMs] for everything Yeah and when I was in college, StackOverflow was full of questions like “how do I add 2 numbers with jQuery”. This is normal. The newbies don’t know what they’re doing and with time they will get enough hard knocks to learn. We’ve all gone through this and still are in areas that are new to us. LLMs aren’t gonna solve the fundamentals: Seniors still gotta senior and newbies still gotta learn.
- vjvjvjvjghv 1y ago
  Using LLMs for universal computing would be crazy inefficient. On top of that they aren't deterministic (at the moment) so building systems on top of that would be shaky
- lloydatkinson 1y ago
  I can’t believe such hyperbolic fantasies are on HN now too.
- eldenring 1y ago
  Hmmmm I don't think that would make sense. Closest analogy is working with humans. The easiest way to work with a human isn't a thin, limited api, but rather to give them context and work together (employment). I think the future of software will look more like Claude Code. Lets the model work in a similar space as us, where it can intelligently seek out information and use tools as a human would.
- narrator 1y ago
  LLMs are already hard to interpret as it is. Why not just have some minimal linux so at least someone with a linux background can observe and easily make sense of its actions? If we make an unfamiliar opaque virtual machine, it's likely to be even less user friendly.
- moduspol 1y ago
  CPUs are deterministic! I think it’s going to be inherently limiting how far you can abstract above something non-deterministic.
  - mehulashah 1y ago
    Agree. The non-determinism is a problem. Perhaps systems thinking can help here. After all, traditional computer systems often behave non-deterministically and we’ve learned to make them more reliable.
mococa 1y ago
I would say we need a webassembly VM/runtime.
nhod 1y ago
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 https://news.ycombinator.com/item?id=45066395
- ijk 1y ago
  I think the main difference is that sandboxing and simplifying the LLM's access to tools and data tends to be core functionality, whereas for XR it is more about performance and developer experience. I'm going to put a lot of work in anyway to keep the LLM from accidentally overwriting the code running it or messing with customer data incorrectly or not being overwhelmed with implementation details; having a standard for this makes it much easier and lets me rely on other people's model training. If it's merely that I have to train a dev on an XR SDK, I can pay them a salary or encourage schools to teach it. AI needs an team for an R&D project and compute time, which can get a lot more expensive at the high end.
- charcircuit 1y ago
  This article does not make a case for writing a new operating system. Building an execution environment for an AI to operate in is completely different from creating a new operating system from scratch designed to be optimized for an AI use case.
  - deleted 1y ago
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- 7373737373 1y ago
  WebAssembly with its sandboxing-by-default paradigm is pretty much halfway there, just need a well defined interface to transfer data and access rights between instances, and creating new instances from others.
ilaksh 1y ago
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 https://mindroot.io
Imustaskforhelp 1y ago
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 https://github.com/libriscv/kvmserver
bitexploder 1y ago
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”.
- wmorgan 1y ago
  Untrusted userspace is exactly right. I’d expect these approaches to help on the margin but the authors oversell their point using words like “guarantee.” Control tool access like OSes enforce file permissions: I understand it’s a metaphor, but also isn’t the track record of OSes here pretty bad? Check whether the agent is allowed to use the booking tool: so a web browser? Isn’t a browser a pretty powerful general-purpose tool, which by the way could also expose the agent to, like, a jailbreak? > As such, security researchers have to devise new mitigations to prevent AI models taking adversarial actions even with the virtual machine constraints. An understated reminder that yes, we really ought to solve alignment.
rbren 1y ago
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 https://github.com/All-Hands-AI/OpenHands
- Ancapistani 1y ago
  I have nothing but good things to say about OpenHands . I’ve been using it daily for months now - some local, some CLI, some cloud-based - and while it’s not quite as polished as Devin, it’s as capable and is growing fast. All Hands is incredibly friendly and responsive to feedback as well, and that means a lot.
eksrow 1y ago
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.
shwaj 1y ago
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.
- spankalee 1y ago
  I absolutely agree in spirit, and I love Fuchsia's design because of this, but I didn't see it happening. Rather than requiring a new OS, I think a Fuchsia-like system based on WASM and WASI components, that can be hosted from the cloud to the phone, is likely the way this goes.
  - mandalander90 1y ago
    I think the opposite, of course this system is also suitable for such an application, but we definitely need such a system on devices directly so that we can use Local devices for smarter communication with each other.Fuchsia os has many advantages that would not be possible without this system in the home ecosystem.
armcat 1y ago
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.
- Ancapistani 1y ago
  That’s also how Devin works, and OpenHands. The agent running in a VM - at least by default - was a key feature during the AI pilot I ran a few months ago.
- hhh 1y ago
  It runs in Kubernetes, on AKS (Azure Kubernetes) with some gvisor stuff. Though Jupyter if i recall.
  - energy123 1y ago
    `to=python.exec` is how it runs python code, and `to=container.exec` is how it runs bash commands, with attached files showing up in `/many/data`. Unfortunately the stdout is heavily truncated prior to being shown to the model so it's not a hack for longer context via printing file attachment's contents.
- valenterry 1y ago
  Not really. Now imagine you run two AIs (like ChatGPT) on your machine or on a server. You maybe even want them to cooperate on something. How do you do that? Right, you cannot, there is no standard, no interoperability, nothing.
liveoneggs 1y ago
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.
- meindnoch 1y ago
  We need a modern and scalable solution! Also, it has to support emojis in variable names.
- jackthetab 1y ago
  Since I'm interested in local, small-use [1] cases of AI, what about the byte code VMs is not AI-scale? And what is AI-scale? [1] I'm looking at single-person/small office LLMs to do simple jobs: summarize these pdfs, structure this data, help drafting a document, that sort of thing, not a be-all end-all monster. Think of a bunch of highly intelligent Python scripts as opposed to Microsoft Office.
ayende 1y ago
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.
- martin-t 1y ago
  I know this is just an example but why shouldn't employee compensation and benefits be visible to coworkers? If the knowledge is one-sided, then so is the ability to negotiate. This benefits nobody except the company which already had an advantageous position in negotiations.
  - rogerrogerr 1y ago
    “Benefits” info may include protected health info depending on the breadth. Like how much of your deductible you’ve used and how you answered the annual “do you smoke” question. What benefits an employee is _eligible_ for - sure, no problem with that being public. What they chose and how they’re using them should be protected. (Imagine finding out a coworker you thought was single is on the spouse+benefits plan!)
    - exe34 1y ago
      > Imagine finding out a coworker you thought was single is on the spouse+benefits plan!) This would cause me to.... do a double take?
  - procaryote 1y ago
lvl155 1y ago
Does AI need a vm OR can AI emulate a vm all in one shot? I think the latter is going to happen.
martini333 1y ago
I'm not reading a article on a web site that hijacks scroll.
- cpncrunch 1y ago
  Yes, I found that very ironic and annoying.
mathiaspoint 1y ago
You don't need a VM for your agent, just a separate user account is plenty.
junail 1y ago
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jmount 1y ago
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.
- Jweb_Guru 1y ago
  I agree that it seems like an extraordinarily difficult problem, but I don't find this particular dismissal convincing. It's much easier to reject something potentially unsafe than it is to generate something safe. That's the basis for all sound type systems and scales to extremely sophisticated types (e.g. it is fairly to define the type representing the Collatz Conjecture, and I think most people would agree that it is perhaps slightly more difficult to find an inhabitant of that type). In this case, I don't even know if we're in the paradigm of "hard to satisfy types"--a lot of the time you can for example probably use an autobooking feature to get something you'd be okay with as a backup, but since you know it is suboptimal you still want to try to do better if possible. There are also plenty of real world control systems which perform fairly involved calculations, but still perform some basic sanity limits checks on the inputs and outputs to make sure that if the calculations screwed up, things don't fail catastrophically. In such cases the limits are much easier to define than a spec for how the whole thing works.
singron 1y ago
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.
- YetAnotherNick 1y ago
  Yes by their definition the current AI agents are running in VM already. e.g. MCP host could prompt the user for executing something and we can also have rules like in claude code to automatically allow some pattern of command.
- Animats 1y ago
  > This article is so devoid of specifics, I'm not entirely sure I know what they are proposing. Yes. Are they proposing a virtual machine execution engine? Docker for LLMs? Or what? This looks like some kind of packaging thing. Badly designed packaging systems are a curse. Look at how many Python has gone through.
  - kookamamie 1y ago
    "Like dude, a JVM for AIs!" - it reads like that.
- user3939382 1y ago
  I read VM and immediately took it to be very adjacent to sandboxing. The article says “isolation”. To me there was neither ambiguity nor a lack of detail. The problem is the author’s point is obvious and solution imperfect. Okay you’re in a sandbox at the OS or let’s even say hardware level. Whoops the agent found AWS CLI with IAM not setup right. The remote boundaries are at least as complex though not unique to automated agents. I don’t see any new insights. The terminology was not the issue.
athrowaway3z 1y ago
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.
- jondwillis 1y ago
  I’m with you for the most part. A lot of, but certainly not all or the security risks are present regardless of whether or not you’re in a VM. I think defense in depth will eventually matter more, but there are a LOT of low-hanging fruit for attackers right now when it comes to turning AI agents against their users, which is what I think you’re alluding to!
- EnPissant 1y ago
  Once you have a tool to edit files, you pretty much lose all security.
- CuriouslyC 1y ago
  Virtual machines contain the blast radius. A good agent will be able to take advantage of zero days from within your system to crack you no problem, being a user makes this really easy. You'd have to carefully firewall its knowledge, but there are so many ways to get stuff on the internet (i.e. ask to download an encrypted version of the file in an obfuscated way from a service that can get past the gatekeeper AI). These things are going to be scary good at cracking systems, trust me, you are going to want things to be ironclad.
- cosmic_cheese 1y ago
spankalee 1y ago
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 https://arxiv.org/pdf/2505.23643
- fullstackwife 1y ago
  "Workflow" is the aspect we should try to eliminate, and a LLM+VM combo allows you to do that. Workflow means you provide tools to the LLM, and ask to make use of them to achieve the goal. This works well already, but it fails whenever an unusual problem appears that is not covered by your predefined set of tools. Another issue is that the workflow based approach is always linear even if it is a DAG, or even if you have some kind of loop. The next step is to not provide any tools to the LLM, and ask it to invent them on-the-fly. Some problems need to be brute-forced.
- socalgal2 1y ago
  Sounds like https://microsoft.github.io/wassette/ https://microsoft.github.io/wassette/
kookamamie 1y ago
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.
kachapopopow 1y ago
We do, it's called running it in docker.
CuriouslyC 1y ago
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.
shykes 1y ago
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 https://docs.dagger.io/features/llm
perlgeek 1y ago
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).
polotics 1y ago
is this just describing in theory what bytebot.ai does in practice? i'm a user just started hacking on it
slashdave 1y ago
Why is the author conflating a virtual machine (a deployment mechanism) with security?
tylerhou 1y ago
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.
kingkawn 1y ago
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
zmmmmm 1y ago
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.
saagarjha 1y ago
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.
tritondev 1y ago
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 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)
Michael_Keller 1y ago
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234 points by azhenley 1y ago

bobrx4 1y ago

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deleted 1y ago

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mehulashah 1y ago

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.

csmpltn 1y ago
This is the wrong take. There's a point beyond which LLMs are an overkill, where a simple script or a "classic" program can outdo the LLM across speed, accuracy, scalability, price and more. LLMs aren't supposed to solve "universal computing". They are another tool in the toolbox, and it's all about using the right tool for the problem.
- mehulashah 1y ago
  I shared your opinion for a while. But, that’s not what’s happening. People are using them for everything. When they do, expectations are set. Vendors will adjust and so will the rest of the industry. It’s happening.
  - kylebenzle 1y ago
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  - baby_souffle 1y ago
    > People are using them for everything Let’s see if that continues to be the case after some time. On a long enough timeline, that 100 line python script that is deterministic is going to beat the non deterministic llm. They are a tool. They re not an omnitool.
    - Imustaskforhelp 1y ago
      If all you have is a hammer, everything looks like a screw. The problem is not the tool, the problem are the people selling the hype, the people accepting the hype as it is and this "everyone" person who I keep on listening about, I wonder why his takes are often times wrong but he doesn't accept them... People can be wrong, People are wrong in a lot of contexts, I don't think the world is efficient in this sense. We are emotional beings, sell us hype and we would accept it and run with it. Agree on how it is not an omnitool. Why are we going towards an inferior product (AI for VMS??) when this doesn't make sense when we already have superior product (I think) I guess somethings don't make sense and if everyone jumps in a well, most people would follow it (maybe including myself, if I didn't want to be contrarian in such sense, my knowledge is really limited regarding AI so all of the above statements can be wrong and hey I am wrong I usually am)
    - IX-103 1y ago
      They're pretty close to being an omnitool. But like any tool you need to know how to use it. Asking it to do some common task is probably silly when you should be asking it to write a program to do the common task (and add the program to its list of tools if it works).
  - kibwen 1y ago
    At the risk of being blunt, this comment reads like someone in the throes of religious euphoria. It makes no sense to call LLMs "the new universal computing machines". Please take a step back and reevaluate the media bubbles you're pickling your brain in.
    - mehulashah 1y ago
      Trust me. I’m usually the last to jump on a bandwagon. That said, this is not just my take, but the take of many others that I trust. Andrej Karpathy, Joseph Hellerstein, etc.
    - IX-103 1y ago
      There was a time when everybody did arithmetic by hand and "calculator" was a profession. There was a time when spreadsheets were made with ink and paper. There was a time when all programs were written in assembly. Now, most arithmetic is done by computers, spreadsheets are done by computers, and almost all assembly language is written by computers. People have moved on to higher level programming languages for communicating ideas to computers. Would it really be that surprising to learn that in the future people use natural language to speak with computers? The company I work for went through a big AI push about a month ago. Before then, almost no code was written by AI. Now, it's the majority of code. I'm not saying that AI coders will be able to replace people, because the AI honestly is just not as capable - if it were interviewing for my company, I would not hire it. But the thing is that I can go from design doc to prototype using less than a day and $10 of tokens. Sure I have to make corrections and rewrite some of the more fiddly bits, but it saves me a ton of typing. And LLMs are not limited to programming. Any computer-based task that you would allow an unpaid intern to complete for you would be a reasonable fit for AI.
  - greenavocado 1y ago
    We need fusion power plants as soon as possible because pretty soon it we will need to boil the oceans to write a Hello World in a hypothetical LLM VM, nevermind actually running the thing, which will require a Dyson Sphere at minimum
    - dingnuts 1y ago
      well Sam A said that is the plan
  - Swizec 1y ago
    > People are using them [LLMs] for everything Yeah and when I was in college, StackOverflow was full of questions like “how do I add 2 numbers with jQuery”. This is normal. The newbies don’t know what they’re doing and with time they will get enough hard knocks to learn. We’ve all gone through this and still are in areas that are new to us. LLMs aren’t gonna solve the fundamentals: Seniors still gotta senior and newbies still gotta learn.
vjvjvjvjghv 1y ago
Using LLMs for universal computing would be crazy inefficient. On top of that they aren't deterministic (at the moment) so building systems on top of that would be shaky
lloydatkinson 1y ago
I can’t believe such hyperbolic fantasies are on HN now too.
eldenring 1y ago
Hmmmm I don't think that would make sense. Closest analogy is working with humans. The easiest way to work with a human isn't a thin, limited api, but rather to give them context and work together (employment). I think the future of software will look more like Claude Code. Lets the model work in a similar space as us, where it can intelligently seek out information and use tools as a human would.
narrator 1y ago
LLMs are already hard to interpret as it is. Why not just have some minimal linux so at least someone with a linux background can observe and easily make sense of its actions? If we make an unfamiliar opaque virtual machine, it's likely to be even less user friendly.
moduspol 1y ago
CPUs are deterministic! I think it’s going to be inherently limiting how far you can abstract above something non-deterministic.
- mehulashah 1y ago
  Agree. The non-determinism is a problem. Perhaps systems thinking can help here. After all, traditional computer systems often behave non-deterministically and we’ve learned to make them more reliable.

mococa 1y ago

I would say we need a webassembly VM/runtime.

nhod 1y ago

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

ijk 1y ago
I think the main difference is that sandboxing and simplifying the LLM's access to tools and data tends to be core functionality, whereas for XR it is more about performance and developer experience. I'm going to put a lot of work in anyway to keep the LLM from accidentally overwriting the code running it or messing with customer data incorrectly or not being overwhelmed with implementation details; having a standard for this makes it much easier and lets me rely on other people's model training. If it's merely that I have to train a dev on an XR SDK, I can pay them a salary or encourage schools to teach it. AI needs an team for an R&D project and compute time, which can get a lot more expensive at the high end.
charcircuit 1y ago
This article does not make a case for writing a new operating system. Building an execution environment for an AI to operate in is completely different from creating a new operating system from scratch designed to be optimized for an AI use case.
- deleted 1y ago
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7373737373 1y ago
WebAssembly with its sandboxing-by-default paradigm is pretty much halfway there, just need a well defined interface to transfer data and access rights between instances, and creating new instances from others.

ilaksh 1y ago

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 https://mindroot.io

Imustaskforhelp 1y ago

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 https://github.com/libriscv/kvmserver

bitexploder 1y ago

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”.

wmorgan 1y ago
Untrusted userspace is exactly right. I’d expect these approaches to help on the margin but the authors oversell their point using words like “guarantee.” Control tool access like OSes enforce file permissions: I understand it’s a metaphor, but also isn’t the track record of OSes here pretty bad? Check whether the agent is allowed to use the booking tool: so a web browser? Isn’t a browser a pretty powerful general-purpose tool, which by the way could also expose the agent to, like, a jailbreak? > As such, security researchers have to devise new mitigations to prevent AI models taking adversarial actions even with the virtual machine constraints. An understated reminder that yes, we really ought to solve alignment.

rbren 1y ago

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 https://github.com/All-Hands-AI/OpenHands

Ancapistani 1y ago
I have nothing but good things to say about OpenHands . I’ve been using it daily for months now - some local, some CLI, some cloud-based - and while it’s not quite as polished as Devin, it’s as capable and is growing fast. All Hands is incredibly friendly and responsive to feedback as well, and that means a lot.

eksrow 1y ago

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.

shwaj 1y ago

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.

spankalee 1y ago
I absolutely agree in spirit, and I love Fuchsia's design because of this, but I didn't see it happening. Rather than requiring a new OS, I think a Fuchsia-like system based on WASM and WASI components, that can be hosted from the cloud to the phone, is likely the way this goes.
- mandalander90 1y ago
  I think the opposite, of course this system is also suitable for such an application, but we definitely need such a system on devices directly so that we can use Local devices for smarter communication with each other.Fuchsia os has many advantages that would not be possible without this system in the home ecosystem.

armcat 1y ago

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.

Ancapistani 1y ago
That’s also how Devin works, and OpenHands. The agent running in a VM - at least by default - was a key feature during the AI pilot I ran a few months ago.
hhh 1y ago
It runs in Kubernetes, on AKS (Azure Kubernetes) with some gvisor stuff. Though Jupyter if i recall.
- energy123 1y ago
  `to=python.exec` is how it runs python code, and `to=container.exec` is how it runs bash commands, with attached files showing up in `/many/data`. Unfortunately the stdout is heavily truncated prior to being shown to the model so it's not a hack for longer context via printing file attachment's contents.
valenterry 1y ago
Not really. Now imagine you run two AIs (like ChatGPT) on your machine or on a server. You maybe even want them to cooperate on something. How do you do that? Right, you cannot, there is no standard, no interoperability, nothing.

liveoneggs 1y ago

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.

meindnoch 1y ago
We need a modern and scalable solution! Also, it has to support emojis in variable names.
jackthetab 1y ago
Since I'm interested in local, small-use [1] cases of AI, what about the byte code VMs is not AI-scale? And what is AI-scale? [1] I'm looking at single-person/small office LLMs to do simple jobs: summarize these pdfs, structure this data, help drafting a document, that sort of thing, not a be-all end-all monster. Think of a bunch of highly intelligent Python scripts as opposed to Microsoft Office.

ayende 1y ago

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.

martin-t 1y ago
I know this is just an example but why shouldn't employee compensation and benefits be visible to coworkers? If the knowledge is one-sided, then so is the ability to negotiate. This benefits nobody except the company which already had an advantageous position in negotiations.
- rogerrogerr 1y ago
  “Benefits” info may include protected health info depending on the breadth. Like how much of your deductible you’ve used and how you answered the annual “do you smoke” question. What benefits an employee is _eligible_ for - sure, no problem with that being public. What they chose and how they’re using them should be protected. (Imagine finding out a coworker you thought was single is on the spouse+benefits plan!)
  - exe34 1y ago
    > Imagine finding out a coworker you thought was single is on the spouse+benefits plan!) This would cause me to.... do a double take?
- procaryote 1y ago

lvl155 1y ago

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

martini333 1y ago

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

cpncrunch 1y ago
Yes, I found that very ironic and annoying.

mathiaspoint 1y ago

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

junail 1y ago

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jmount 1y ago

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.

Jweb_Guru 1y ago
I agree that it seems like an extraordinarily difficult problem, but I don't find this particular dismissal convincing. It's much easier to reject something potentially unsafe than it is to generate something safe. That's the basis for all sound type systems and scales to extremely sophisticated types (e.g. it is fairly to define the type representing the Collatz Conjecture, and I think most people would agree that it is perhaps slightly more difficult to find an inhabitant of that type). In this case, I don't even know if we're in the paradigm of "hard to satisfy types"--a lot of the time you can for example probably use an autobooking feature to get something you'd be okay with as a backup, but since you know it is suboptimal you still want to try to do better if possible. There are also plenty of real world control systems which perform fairly involved calculations, but still perform some basic sanity limits checks on the inputs and outputs to make sure that if the calculations screwed up, things don't fail catastrophically. In such cases the limits are much easier to define than a spec for how the whole thing works.

singron 1y ago

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.

YetAnotherNick 1y ago
Yes by their definition the current AI agents are running in VM already. e.g. MCP host could prompt the user for executing something and we can also have rules like in claude code to automatically allow some pattern of command.
Animats 1y ago
> This article is so devoid of specifics, I'm not entirely sure I know what they are proposing. Yes. Are they proposing a virtual machine execution engine? Docker for LLMs? Or what? This looks like some kind of packaging thing. Badly designed packaging systems are a curse. Look at how many Python has gone through.
- kookamamie 1y ago
  "Like dude, a JVM for AIs!" - it reads like that.
user3939382 1y ago
I read VM and immediately took it to be very adjacent to sandboxing. The article says “isolation”. To me there was neither ambiguity nor a lack of detail. The problem is the author’s point is obvious and solution imperfect. Okay you’re in a sandbox at the OS or let’s even say hardware level. Whoops the agent found AWS CLI with IAM not setup right. The remote boundaries are at least as complex though not unique to automated agents. I don’t see any new insights. The terminology was not the issue.

athrowaway3z 1y ago

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.

jondwillis 1y ago
I’m with you for the most part. A lot of, but certainly not all or the security risks are present regardless of whether or not you’re in a VM. I think defense in depth will eventually matter more, but there are a LOT of low-hanging fruit for attackers right now when it comes to turning AI agents against their users, which is what I think you’re alluding to!
EnPissant 1y ago
Once you have a tool to edit files, you pretty much lose all security.
CuriouslyC 1y ago
Virtual machines contain the blast radius. A good agent will be able to take advantage of zero days from within your system to crack you no problem, being a user makes this really easy. You'd have to carefully firewall its knowledge, but there are so many ways to get stuff on the internet (i.e. ask to download an encrypted version of the file in an obfuscated way from a service that can get past the gatekeeper AI). These things are going to be scary good at cracking systems, trust me, you are going to want things to be ironclad.
cosmic_cheese 1y ago

spankalee 1y ago

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 https://arxiv.org/pdf/2505.23643

fullstackwife 1y ago
"Workflow" is the aspect we should try to eliminate, and a LLM+VM combo allows you to do that. Workflow means you provide tools to the LLM, and ask to make use of them to achieve the goal. This works well already, but it fails whenever an unusual problem appears that is not covered by your predefined set of tools. Another issue is that the workflow based approach is always linear even if it is a DAG, or even if you have some kind of loop. The next step is to not provide any tools to the LLM, and ask it to invent them on-the-fly. Some problems need to be brute-forced.
socalgal2 1y ago
Sounds like https://microsoft.github.io/wassette/ https://microsoft.github.io/wassette/

kookamamie 1y ago

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.

kachapopopow 1y ago

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

CuriouslyC 1y ago

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.

shykes 1y ago

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 https://docs.dagger.io/features/llm

perlgeek 1y ago

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).

polotics 1y ago

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

slashdave 1y ago

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

tylerhou 1y ago

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.

kingkawn 1y ago

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

zmmmmm 1y ago

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.

saagarjha 1y ago

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.

tritondev 1y ago

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 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)

Michael_Keller 1y ago

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