It's also the first project I've used where Claude Code is the setup and configuration interface. It works really well, and it's fun to add new features on a whim.
This threat model is concerned with running arbitrary code generated by or fetched by an AI agent on host machines which contain secrets, sensitive files, and/or exfoliate data, apps, and systems which should not be lost.
What about the threat model where an agent deletes your entire inbox? Or sends your calendar events to a server after prompt injection? Bank transfers of the wrong amount to the wrong address etc. all these are allowed under the sandboxing model.
We need fine grained permissions per-task or per-tool in addition to sandboxing. For example: "this request should only ever read my gmail and never write, delete, or move emails".
Sandboxes do not solve permission escalation or exfiltration threats.
My posts about these aspects of agent security get zero engagement (not even a salty "vibe slop" comment, lol), so ironically security is the thing everyone's talking about, but most people don't know enough to understand what they need.
The Security Model: Design for Distrust
I wrote about this in Don’t Trust AI Agents: when you’re building with AI agents, they should be treated as untrusted and potentially malicious. Prompt injection, model misbehavior, things nobody’s thought of yet. The right approach is architecture that assumes agents will misbehave and contains the damage when they do…It does not really matter.
IMHO, until you figure out useful ways to spend tokens to do useful tasks the runtime should be a second thought.
As far as security goes, running LLM in a container in just simply not enough. What matters is not what files it can edit on your machine but what information it can access. And the access in this case as far as these agents are concerned is basically everything. If this does not scare you you should not be thinking about containers.
The biggest one (as Karpathy notes) is having skills for how to write a (slack, discord, etc) integration, instead of shipping an implementation for each.
Call it “Claude native development” if you will, but “fork and customize” instead of batteries-included platforms/frameworks is going to be a big shift when it percolates through the ecosystem.
A bunch of things you need to figure out, eg how do you ship a spec for how to test and validate the thing, make it secure, etc.
How long before OSs start evolving in this way? You can imagine Auto research-like sharing and promotion upstream of good fixes/approaches, but a more heterogenous ecosystem could be more resistant to attacks if each instance had a strong immune system.
The strength of open source software is collaboration. That many people have tried it, read it, submitted fixes and had those fixes reviewed and accepted.
We've all seen LLMs spit out garbage bugs on the first few tries. I've written garbage bugs on my first try too. We all benefit from the review process.
I would rather have a battle tested base to start customizing from than having to stumble through the pitfalls of a buggy or insecure AI implementation.
It's better if your app's description just tells me what it does in a direct way using plain language. It's fine to tell me it's an alternative to something, but that should be in addition to rather than instead of your own description.
On Linux, however, I absolutely don't want a hypervisor on my quite underpowered single-board server. Linux namespaces are enough for what I want from them (i.e. preventing one of these agent harnesses to hijack my memory, disk, or CPU). I wonder why neither OpenClaw nor NanoClaw seem to offer a sanely configured, prebuilt, and frequently updated Docker image?
For me, it's my diet and workout buddy. It knows my goals, keeps me on track, does meal planning for me, gives me grocery lists, logs what I eat, when I exercise... anything I want so I don't slack off.
I've enhanced Nanoclaw quite a bit. Moved it to Apple containers (shipped with this Skill already). Then I wrote an API for Nanoclaw to use (food log, workouts, etc), then implemented long-term memory using LanceDB (because I was tired of repeating myself!).
In other words, Claude is the compiler.
whalesalad•1h ago
causal•1h ago
But I fundamentally agree that there is just too much overlap between what makes claws useful and what makes them insecure.
wild_egg•1h ago
access control, provisioning, and delegation have been solved for a very long time now.