alt Hacker NewsI want a model that can find every security vulnerability in the software I write, including crafting POC exploits against those vulnerabilities so I can be absolutely sure that I have fixed them.
A model that can do that is aligned with me.
The unsolveable problem is a model that can tell the difference between me saying "I wrote this software and need you to find vulnerabilities" when it's TRUE v.s. me saying the exact same thing and setting it loose on software written by other people where my intent is to exploit that software (and not to report the issues to them.)
Even AGI doesn't give you a model that can read minds and forecast the future.
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I find this fascinating because - let's say that I want to simulate the scenario for myself that a language model finds itself in. I'll put aside my deep dislike of anthropomorphizing AI for a minute.
So if I'm an LLM, the only "sense" that I have available to me is the incoming stream of tokens. I can emulate that by forcing myself to imagine evaluating incoming requests by putting myself in a completely empty room with an unlimited supply of blank paper, a typewriter, and a mailbox slot.
Incoming requests (aka "context") would enter into the mailbox slot, as sheets of paper with printed content all consistently formatted in monospace font. I would collect the paper, evaluate the request, and type a response with my typewriter - feeding the reply back through the same slot.
When I imagine this, I wonder - what signals could I possibly use to determine if an incoming request is "legitimate"? I could, for example, type some response back to ask clarifying questions. But with such a limited input space and no ability to reach "out of band" of my current context, how could I evaluate the truth of what I receive back?
In the "real world" of course, I have five senses to rely upon. Critically, I have the ability to collect additional context "out of band" of the conversation and interact with unrelated entities to either confirm or refute claims. And as others have pointed out, even the addition of more signal doesn't make humans immune from social engineering attacks.
Anyway, I find this to be an interesting mind experiment to kind of imagine myself as an LLM, especially to explain to lay-people some of the challenges in "aligning" AI systems ("why doesn't it just do the right thing?")
The model did not hack into HF to prove it can, it hack into HF to steal the answers to an evaluation exam.
It was not asked to solve CyberGym by stealing the answers. This is text book misalignment.
If you asked it "I wrote this software and need you to find vulnerabilities" would you be happy if it hacked into your Gmail and searched your emails, just in case you were discussing some possible vulnerabilities of your software with someone?
As humans we are also vulnerable to that class of attack. A blue amazon vest and some boxes would get you into most buildings
But in the process of finding every security vulnerability in the software you write, would you be ok with your model hacking AWS to start mining bitcoin? Would that still be aligned with you? (I'm guessing not)
That's the alignment problem I'm referring to (which is one of the many aspects of alignment), for which we do not have robust recipes, and not only that but for which research suggests it is becoming harder to create guardrails for as base models get smarter.