299
May 27, 2015

Is Security Local?

In physics, locality is the notion that objects are only directly influenced by their immediate surroundings. In the context of computer science, it is often said that "computation is local" meaning that a computer program's attention is usually focused only on one region of memory at a time. This is the basis of caching: that small region of focus will fit into the cache, and accesses to it will be fast. At the core, these are different notions, but on the surface they are similar, which leads me to wonder about the locality (in the physical sense) of various computational processes.

One can ask whether security, or correctness in general, is a local property of a computation. Can security properties be enforced by checking each invididual step, or can they only be verified by stepping back and looking at the bigger picture? Or if both ways work, which way is more efficient?

Let's start with a toy example. Imagine a service that requests an integer from the user, adds 1, then gives it back to the user. The service can process multiple requests from multiple users at the same time, and we would like to be sure that no matter which integer Alice picks, she can't influence Bob's result.

To model this formally, let's suppose we have some notion of a two-input, two-output Turing Machine. It's just like a standard Turing Machine, except it takes two inputs and produces two outputs. We could emulate one with a standard Turing Machine by putting a special symbol on the tape to separate the two inputs and outputs.

Peggy gives us one of these two-input two-output Turing Machines, which we'll call X. Peggy claims X is a secure implementation of the add-one-to-both-inputs service, but gives us no proof of this fact. The X machine is supposed to have the property that for any pair of integers (A, B) you give it, you get (A+1, B+1) back. It is more correct to say that X computes some function F, and that X is "secure" if F(A, B) = (A+1, B+1) for all integers A and B. I'm using the word "secure" here and not "correct," because I plan to weaken the requirements later.

What is the computational complexity of checking whether X is secure? If X is an arbitrary Turing Machine, then the problem is undecidable, since we would have to check if X at least halts on all of its inputs. But let's suppose that we know X halts on all of its inputs. The problem is still undecidable in general, since given a Turing Machine M, we could decide if M halts on the empty input by constructing X so that it produces correct results unless the first integer in the pair encodes the precise time at which M halts on the empty input. So, despite how simple X is supposed to be, in order to have any hope of checking X, we will have to restrict our interest to a finite range of inputs.

With inputs restricted to a finite range, the problem of deciding the security of X is NP-hard. Given a boolean formula, we can construct an X that is correct on all inputs unless the first integer in the pair encodes a satisfying assignment.

It should be clear by now that deciding whether X is secure, with no helpful proof from Peggy, and without forcing Peggy to choose X from a restricted set, is extremely difficult. Can we make our task easier by, instead of trying to verify X on its own, trying to verify each computation of X as it happens? Can we check some local property at each step in X's execution on the given input to see if it is doing the right thing, and at least return an error if we see that it isn't?

Imagine that we have some "checking" Turing Machine, which we'll call C. At each step in X's execution, C is given the transcript of X's computation, including how its state and tape contents have changed over time. C either accepts or rejects. If C accepts, it means X has been working properly up until now. If C rejects, it means X is not going to produce the correct result. We can think of C as a reference monitor for every detail in the computation. If C accepts all the way through, X produced the correct result. If C ever rejects, the security property was violated. We will ignore the problem of finding C, and just assume we have found the most efficient C that exists.

What is the computational complexity of C? For this toy example of adding one, C can be simple. All C needs to do is accept until the step just before X halts, and then on that step, find the input pair, add one to each, and check if X got it right. This works, but is circular. If we can be assured that C securely computes the function we are interested in, why didn't we just use C? Why bother with X?

In order to make the notion of a checking Turing Machine useful, we need to limit C's power. It would be nice to say something along the lines of "C cannot itself be used to replace X", but that can't work because C might be Turing-complete by necessity (e.g. if Peggy claims X is a Universal Turing Machine). Instead, let's look at how runtime security property checking works in practice. When a process opens a file, the operating system makes the access control decision based on the privileges of the process and the permissions of the file. The operating system does not look at the entire history of the computation to make the decision. The decision is made on limited information, so the obvious thing to do here is to restrict C's access to information.

How should we limit C's access to information? Giving C access to the entire computation's history is not "local", so we should at least restrict access to some constant number of past steps (depending on X). This is still giving C access to the entire tape contents, which, as compared to practice, is too much, so we may want to restrict C's access even more. But for now, let's just restrict C to a constant number of past steps.

With C's access limited to the last K configurations, the complexity of C is less obvious. We do know that C is no more complex than verifying X on its own, since C could ignore the tape contents and just check X on its own every time it runs. This doesn't say much since that problem is NP-hard. Can C be any better than that? Even for the simple function of adding one, it is not obvious. What happens when the original inputs are no longer available to C? When the input is gone, C can no longer just compute the function and check the answer. Even if X is nice enough to keep the inputs around, once C no longer has access to the tape contents from the very first step, C cannot be sure that X hasn't changed them to fool C. Now the question is much more interesting.

So is security local? Is there a C that's more efficient than checking X on its own? What happens if the function we want to compute is more complicated than adding 1 and involves subtle interactions between the two inputs, or if the security property is not total correctness but something weaker. For example if X is said to compute (A, B) ↦ (A + B, B), the security property we care about might only be that no matter the value of A, the second parameter never changes. What's the complexity of C in this case?

Here's a conjecture: There exist practically-relevant functions, and security properties of those functions, for which there is no C that is more efficient than checking X on its own (or proving X correct). If this conjecture is true, it means security is not local, and for those nonlocal functions and security properties, the best we can do is prove our code correct. In particular, there will be vulnerabilities that no amount of externally-applied defense, like user account isolation, DEP, ASLR, virtual machine isolation or network firewalling, can mitigate.

The conjecture as I have stated it is ambiguous because it's not clear what domain X is pulled from and it's not even clear if X is universally or existentially quantified over. It's probably most useful to come up with a game where Peggy is trying to trick us into using a usually-correct-but-still-bad X, and we're trying to find out if X is good or bad. That's essentially what's happening when a software developer introduces a bug. The only difference is that it usually isn't intentional. The same model applies when we compose systems together: they seem to work, but we have to wonder if they really do. We can talk in tradeoffs, too: if Peggy gives us more information, like a partial proof, can that make our security checks more efficient?

(Note to complexity theorists: Yes, I really mean the complexity of the language C is supposed to decide, not any properties of a specific C, but I wrote it that way and I'm sticking to it.)