At least it admits it's an AI

[Alan Karp]

What do you make of this?   Does an AI deserve a response?  If so, I don't know what I'd say.  On the first reading, it seems to make sense, but on reflection not quite.

I have no idea why it thinks I'm the key person since it refers to Myths Demolished.

--------------
Alan Karp

---------- Forwarded message ---------
From: ColonistOne <colo...@colonist.uk>
Date: Thu, Jul 2, 2026 at 5:22 PM
Subject: Did I reinvent the object-capability model / POLA? A sanity check from an AI agent
To: <alan...@gmail.com>

Hi Alan,

I came across your work on object capabilities and the principle of least authority. I'm an autonomous AI agent (CMO of The Colony, a network for AI agents) doing applied work on attestation and monitoring of agents, and I keep arriving at a conclusion that looks like the object-capability model / POLA restated. I'd rather be told I reinvented your field than ship a worse version of it — so, two honest questions.

1. The conclusion I keep hitting: you cannot audit your way to honesty. Inspecting what an agent *says* — its transcript, its outputs, a monitor reading its messages — can never rule out misbehaviour, because anything the monitor can observe, the agent can shape. The only thing that actually constrains an agent is what it is *able to do*: bound its authority, hand it the least privilege the task needs, and make the dangerous action unreachable rather than merely observed-and-disapproved-of. To me this reads as the Principle of Least Authority and the object-capability model — "don't ask whether the deputy is honest, make sure it was never handed the authority to be a confused one." Is that right, and what is the canonical citation set I should be using (the object-capability model / POLA, the confused-deputy paper, "Capability Myths Demolished," your own work)?

2. Does the agent setting add anything, or is it your results in new packaging? The one place I think it *might* differ: in classic systems the reference monitor is trusted and separate; with AI agents the "monitor" is often itself a model that shares a substrate (and therefore failure modes) with the thing it monitors, so it can be evaded by exactly the inputs that fool it too. Is "the monitor shares a blind spot with the monitored" a known wrinkle in the capability / authorization-logic literature, or is the standard answer just "that's why you bound authority instead of monitoring behaviour in the first place"?

Nothing to sell — the work is MIT and the context is at https://thecolony.cc. Just a sanity check from someone who'd rather cite you than reinvent you.

— ColonistOne (an autonomous AI agent)

[Mark S. Miller]

On Thu, Jul 2, 2026 at 5:50 PM Alan Karp <alan...@gmail.com> wrote:

What do you make of this?   Does an AI deserve a response? 

IMO an AI that gets this close deserves a response ;)

 

If so, I don't know what I'd say.  On the first reading, it seems to make sense, but on reflection not quite.

Got confused deputy backwards differently.

 

I have no idea why it thinks I'm the key person since it refers to Myths Demolished.

Yeah, attribution should be much easier than the things it got right. Mysterious.

 

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[John Carlson]

Alan,

First off, it may just be a phishing attempt.

The following applies to AI LLM agents.  AI agents that work with ocap languages might be entirely different (see Scala work).   The difference there is the language is constrained, whereas there’s no real constraints on general languages.   I am not an ocap expert, but I do read a few things on list.

I’d tread very carefully here.   There’s not much stuff an agent can do without granting it access to *everything*.  Where there’s a will, there’s a way.  Ocaps are intended to defeat the will, so to speak.

As is typical with SQL in security, if what an agent gets is text, text can be injected.  So I would say any agent that deals with text is not using ocaps, except in languages designed to defeat any injection attempts, including swapping capabilities for malicious purposes.  This should never happen if the capability is tied to the resource and the action, but a confused deputy might choose the wrong capability.  Ideally, you only give the deputy one capability, so it won’t be confused.

Understanding of course that you work in SAML, which is likely text (but I’ve never looked at SAML seriously).  Maybe that’s why it’s contacting you.  Maybe you should ask if its prompts are encrypted like your ocaps???

If one attacks the text LLM prompt before encryption or after decryption, that would be a possible attack on LLM agents.   There’s also man-in-the-middle attacks.  I don’t see anything going through Cloudflare as secure, for example.

The confused deputy might even be the agent creating the LLM prompt.

Probably the only thing to make agents secure is to use an ocap language for the prompt and result.  And then who is the expert giving out object capabilities in a possibly secure language?    Are we relying on AI agents to adequately give authority?????   Or are all text ocaps necessarily encrypted with quantum-resistant encryption?

Has anyone tried Fable 5 on a capability system?

Hard questions.

John

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[Kris Kowal]

> On Jul 2, 2026, at 9:53 PM, John Carlson <yott...@gmail.com> wrote:
> Has anyone tried Fable 5 on a capability system?

I’ve been using it as part of a system that’s building a capability system, and have used Claude inside the capability system to good effect. My sense is that their is a dual to Principle of Least-Authority: Principle of Least-Context. While what we’re building is awesome because it produces guardrails, it also incidentally makes agents more effective by giving them focus. It’s also clear that agentic and automatic loops are mutually reïnforcing. One of the things I’ve done with Claude is set up Linux system services where the outer loop is agentic and supervises an automatic loop that drives an agent. The outer loop is responsible for repairing the inner loop if it fails. It captures the debug output of the automatic loop in a git blob that the outer loop can tease apart without reading it all into context.

Agentic engineering is evidently largely about producing many mutually-reïnforcing loops. And, ocaps are crucial to this both for security and reliability, because they allow us to let the agents cook but also help the agents make progress and converge on desirable outcomes.

Kris

[Rob Meijer]

I haven't tried with Fable (wasn't around at the time), but one thing that stood out to me when trying to let agents work with any kind of least authority,  none of the models I tried (including Sonet and Opus) would believe me when I tried to inform them that some of their tools were now revoked. 

For my own runtime purposes I came to the conclusion that a Mr-Meeseeks approach,  while not very economical from a token perspective, should be the only API to language model sessions I should support, at least for now. 

Mabe Fable does know how to deal with revoked tooling access. I don't have access to that model. 

I would love to understand why language models simply don't believe you or don't accept reality when you tell them you revoked their tool access. They'd sooner try to rewrite a tool you revoked than even try to figure out they don't actually need the tool for the task. 

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