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It's been tried so many times before, and it never worked.
The common harnesses also have some sandbox functionality, which although imperfect actually does help contain the blast radius for a lot of things.
The common harnesses also support remote development over SSH, which I and many others use to contain development to a virtual machine.
If your complaint is that LLMs can execute tool calls then you’re never going to be happy with any of these solutions and this turns into another generic anti-LLM complaint.
I'm sure I've already got a dozen people reaching for the reply button, but slow down there, cowboy. I don't think it's even remotely as easy to define as people think. We have a reasonable concept of how to lock them down really tightly, no question, and I expect that most of the answers in the "leap to mind" category match that.
But let's say we'd like them to continue functioning the way they do today. I want my agent to be able to hit the web. I want my agent to be able to read out of its assigned directory sometimes. I want it to be able to hit external resources through MCP servers that have no pragmatic way to know what's going on. And probably most importantly of all, I want my AI to be able to grab from three distinct sources, each of which may be nominally safe on its own, and combine things in a way that may make each of those nominally safe things become unsafe. For example, any ability to read a local file and make a remote request becomes a potential exfiltration mechanism, especially when you remember all the sidechannel ways communication can occur.
I agree that shifting everything on to the user is essentially non-functional. But whereas I feel like I have a reasonable answer to a lot of other security-related problems, it isn't even clear to me what the definition of a secure agent is.
There's an effect I need to put a name on someday, where you can get 10 people in a room to agree to a certain series of words, and they will all leave the meeting thinking they agree, but in fact there is no agreement at all because they all have a different definition of the words that were used. In this case, everyone here is going to go "Oh, yes, certainly, AI agents should be secured." But if you sit down with 10 of us to really do the work of defining exactly what that is, you're going to get 10 different answers. There will be overlap, certainly, but when you get down to the nitty-gritty questions like "OK, the user has explicitly asked the agent to do X by accessing Y and the agent has done so and determined that they need to do Z, which the user clicked "allow all" for, and now the agent has decided that it wants to do T, is T fully covered under that "allow all" or not?" you're not going to get anything like universal agreement across the huge range of Xs, Ys, Zs and Ts that could happen and are relevant... and that's still just one question! It's not the totality of what constitutes a "secure agent".
Defining what a "secure agent" even is is really hard because when it comes to agents, the things that fill in the variables are as arbitrarily complicated as human actions. I haven't fully worked this out but it might be reasonable to say that "agent security" is in reality Turing complete, what with the way they so often throw out fully-fledged programs that you have to approve or reject permissions for.
https://en.wikipedia.org/wiki/Capability-based_security
Thus some agents with higher capabilities can only be run with user oversight at the same time.
Some agents can not be run during some part of the day - for example these agents can not run within two hours of office closing time, and cannot run on weekends.
Maybe also the idea of agents writing code - throwing "out fully-fledged programs that you have to approve or reject permissions for."
Would work better with a capabilities based model where you choose capabilities for the program before hand, meaning the capabilities are not written by agent itself, you read through the code, some of it looks hairy but everything is fine, but oh no dumb human missed the part where agent writes to system32! But luckily enough the program you were expecting actually needed no write capabilities and thus when it tries to go past its assigned capabilities that part of the program fails and the exception is registered.
Googling it seems like lots of people have thought this (at least where Capability based security is concerned), which seems reasonable to me as it also seems pretty self-evident it must be this way. Have not really seen anything about time based controls but then that is probably because I'm not devoting a lot of effort as I am just doing a bit of procrastination to build up the energy to finish something off.
The best case would be putting an agent in a VM and mounting the working directory there. Then you can allow it to run somewhat arbitrary actions while still being able to turn off the vm and restart it in a clean state.
The issue is, of course, that it doesn't fully prevent all possible problems an agent can cause. exfiltration is, IMO, basically impossible to stop. LLMs are exfiltration machines. The basic premise of all of them is "send us your code and a prompt and we'll do something good with it. But also if an agent decides run a command which installs a worm on a device on the network, you are hosed.
github.com/brianv0/formwork
You should be easily able to hide/lock down files, network, and MCP tools from an agent and it shouldn’t be up to the agent.
Also the game was on a timer, and maybe there are some very abusive workplaces where you feel that kind of pressure, but I think most of us actually take the time to understand what a being asked before approving it.
It’s simply a CYA click-thru by the model vendors so their lawyers can say “well you approved it this is on you” when AI does something stupid.
It's just a game, but I found the stats still interesting that I wanted to share back. Even with the warning up front, 1 in 3 threats were missed, and the history log above npm run commands seems to be typically ignored.
I also incorporated the feedback and insights from the previous HN thread, dns_snek's point about npm run in particular. Appreciate everyone who played and shared feedback!
it's designing the environment and invariants so whole categories of failures can not happen at all
the agent ui nagging the user for approval is a ux anti-pattern, we already know how well this works for operating system permission dialogues
Of course the original sense of the noun was "a person who devises things" and shares a root with "ingenious" and carried no connotation of legal credential. That "harm" is more or less restorative to the original meaning of the word.
Context-less self-approval is working well. The failure mode is usually false positives (i.e. safe commands being rejected), not the other way around, with root cause of requesting agent underspecifying context (e.g. not mentioning in the request that it's made on behalf of user etc.)
Thus, I'm running self-approval YOLO modes on state-of-the-art models for quite some time and it didn't bit me. It might, but hey, we're long gone from the age of predictable software development.
One of the core assumptions was that outside of extraordinary circumstances, the expected rate of human error is about 5%. However, the course also provided examples where error rates were significantly lower (eg. nurses in maternity wards would have a much lower than 0.1% error rate when pairing mothers with newborns).
The error rates, of course, also depended on human ability to measure the difference. Since I was studying typography, the printing process was of particular interest. A GOST for offset printing required that the color intensity for each ink of CMYK, for example, should be within +-2.5% range of the intended intensity. This is difficult for someone who doesn't have a lot of experience operating an offset printing machine to spot, but experienced printers have no problem with that.
Most importantly. There was never an acceptable error rate of 33%. Not for anything. If people were likely to make that many errors (eg. because the measurement was too difficult), that product would never have been allowed into production.
So if a basic curl | bash was tweaked to download malware which the agent gets tricked into running the command but it said it was safe, the user would just approve it.
If that number is something like 10%, then we have a really big problem. But, if it's .000001%, then it's pretty vanishing. At some point in between we cross a threshold that puts the risk below many other risks that we routinely take (e.g. trusting npm dependency graphs).
Of course, if it's really that low a percentage, then the entire model of "supervising" via human approval really is fundamentally flawed.