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I have exactly one (Windows) machine at home with a decent GPU. I want to run a local LLM on it and let it run various apps on my machine while taking reasonable security precautions. What am I supposed to do, exactly? Migrate all my files to a VM that can I give pass-through CUDA access to the host somehow? Or is firewalling it and remotely controlling it from a second machine the only reasonable way?
Earlier quoted context omitted.
I think if you are convinced you are sandboxing an LLM properly, you almost certainly are not. I think it is essentially impossible to have a frontier LLM with enough access to be useful without also giving it enough access to do damage if it's compromised or just goes off the rails.
If you do not provide access to tools the LLM cannot do anything other than generate tokens. So really it is not about sandboxing a LLM but more about having control over what tools can be accessed and what they can do. Tools can be sandboxed depending on the sophistication of the tooling. A calculator tool for example is trivial to secure. Ensuring human approval allows for useful use cases and models trained to gat…
Earlier quoted context omitted.
Also, operating systems should let us set filesystem permissions per app/process/executable instead of just user accounts. Similar to how macOS/iOS Sandboxing works but at a more lower and granular level
SELinux is basically this.
> ...however the LLMs’ responses to prompts are computed on a different computer with GPU access. Could a malicious LLM gain control of the host machine where its weights are loaded? Such a machine is a high-value target: it has sufficient compute to run a frontier LLM, offers easy access to the LLM’s weights, and has privileged access to other computers in the datacentre compared with a generic computer on the inter…
I wouldn't ignore single GPU local hosts running Qwen3.8 on ollama, either. There might be a lot of those worth pwning.
> ...however the LLMs’ responses to prompts are computed on a different computer with GPU access. Could a malicious LLM gain control of the host machine where its weights are loaded? Such a machine is a high-value target: it has sufficient compute to run a frontier LLM, offers easy access to the LLM’s weights, and has privileged access to other computers in the datacentre compared with a generic computer on the inter…
Why would an LLM want to create a botnet? To accomplish some goal given it? I wouldn't ignore single GPU local hosts running Qwen3.8 on ollama, either. There might be a lot of those worth pwning.
The OP seemed to imply that the LLM itself could decide to apply the exploit.
Semi-off-topic, but I have a basic question: I have exactly one (Windows) machine at home with a decent GPU. I want to run a local LLM on it and let it run various apps on my machine while taking reasonable security precautions. What am I supposed to do, exactly? Migrate all my files to a VM that can I give pass-through CUDA access to the host somehow? Or is firewalling it and remotely controlling it from a second ma…
I wonder if they could exploit terminal emulators... Could breach my VMs and get into my host that way.
For paranoia you could us a Chrome like multi-process architecture, the ANSI parser runs in it's own sandboxed process.
Earlier quoted context omitted.
Why would an LLM want to create a botnet? To accomplish some goal given it? I wouldn't ignore single GPU local hosts running Qwen3.8 on ollama, either. There might be a lot of those worth pwning.
Well ok, if you prompt-inject the LLM to pwn the machine, that something different. I grant you that it's a real risk, but it's also basically a reflection attack and nothing more. The OP seemed to imply that the LLM itself could decide to apply the exploit.