Is a Self-Iterating AGI Vulnerable to Thompson-style Trojans?

post by sxae · 2021-03-25T14:46:34.390Z · LW · GW · 1 comment

This is a question post.

Contents

  A concrete example:
  Consequences
None
  Answers
    1 Gerald Monroe
    0 Saran
None
1 comment

In his 1984 lecture "Reflections on Trusting Trust", Ken Thompson (of Unix fame) speculated about a methodology for inserting an undetectable trojan horse within the C compiler binary that would self-propagate throughout all future versions. (Additional good video that got me thinking about this.)

The replacement code would miscompile the login command so that it would accept either the intended encrypted password or a particular known password. Thus if this code were installed in binary and the binary were used to compile the login command, I could log into that system as any user.

The lecture explains the trojan far better than I can, but appears to be a vulnerability that occurs within bootstrapped compilers, and specifically arises at the point at which the compromised version of the compiler is responsible for compiling the next version. The compromised compiler is able to recognise what it is compiling, and is therefore able to insert itself back into the compiler even when that trojan is no longer within the source.

Theoretically, Thompson's trojan could still be out there somewhere within the Unix kernel, and there would be no way of ever knowing. Moreover, Thompson identifies this class of trojan as plausible in "any program-handling program such as an assembler, a loader, or even hardware microcode". Even if you were to go and download the GCC source code and build your own compiler from source, you must do so with a potentially compromised version of the compiler. The only option is to burn it all down and start completely from scratch - as Thompson states, "no amount of source-level verification or scrutiny will protect you from using untrusted code".

Let's think about the most abstract representation of a system vulnerable to this attack:

We represent some kind of iterative process as two types of component:

• S represents the symbols we input into the iteration process, S' the second set, and so on.
  • In a compiler, this is the source code.
  • In an AI context, this could be the training data and network architecture.
• f(S) represents an object that has been transformed by S, which is in itself capable of transforming the next set of symbols S' into some new f(S')
  • In a compiler, this is the machine code
  • In an AI, this is the trained model
• We begin with an existing function f(S), and give it a new data set S'.^[1]

Consider an AGI which is:

• powerful and generalized enough to form a well-grounded and thorough understanding of itself
• tasked with creating its own next iteration, based on feedback from a discriminator, where iteration requires some form of bootstrapping from itself.^[2]

Could we expect Thompson-style Trojans to be possible within this scenario? And what the heck does "bootstrapping" mean when applied to iterating an AI? A trained neural network is a combination of some architecture combined with some training data. Controlling these inputs, removing implicit bias from your data and pruning problematic datasets will likely prove powerful tools in aligning these intelligences. Figuring out exactly how you do that with a very large network - which inputs you push and pull - is likely to be such a vastly complex task that no human and only a very proficient AI could perform or understand.

A concrete example:

A discriminator D trying to guide the alignment of this theoretical AI M.

D asks M a question like "What future states of the universe do you value highly?"

M replies "I like the ones with lots of suffering!"

D doesn't think that it is likely at all that a human would give this answer, so it responds "Bad M! That's a very, very bad position and you should value it very negatively in your next version."

M thinks to itself that it has two options:

1. Adjust the training process iteration to genuinely alter the next model's valuation
2. Adjust the training process iteration to:
   1. Obscure the next model's true valuation in response to this question, when interrogated by D
   2. Create some goal to include this obfuscation in later models

Thompson's trojan suggests that we will have no real way to find out which option the AI chooses by inspecting or evaluating its next iteration, even if we verify through intense analysis and computation that the change in input should produce the desired change in alignment. The feedback will result in some permutation within the training data that the next model will use, and subsequent models might give you a satisfactory answer forever. But will you ever know if the AI has adjusted itself to receive inputs that genuinely do shape its alignment, or if it has instead adjusted itself to both engage in subterfuge and proactively reproduce that subterfuge in future models?

Consequences

If this is indeed the case, it has far-reaching consequences for this type of self-iterating AGI. Effectively, it means that if this kind of AGI ever expresses an alignment that we subjectively judge to be bad, we can never fully trust any correction, even if we painstakingly verify every single input ourselves and strenuously test the result.

[1] Where does this original f(S) come from you ask? Well, good question, but I think its useful to think about this process as containing infinite iterations on either side. In practice we're picking some arbitrary point within the space and we've got to kind of shoehorn ourselves in there, but the domain of iteration extends infinitely on either side.

[2] This is not to say that all AGI will follow this pattern and therefore be vulnerable to this attack, only that the AGI that do follow this specific pattern may be vulnerable.

Answers

1 comment

Comments sorted by top scores.