Organizing threat modeling magic

I was inspired to develop and share my thoughts after Adam’s previous post (magical approaches to threat modeling) regarding selection of the threats and predictions. Since a 140 characters limit quickly annoys me, Adam gave me an opportunity to contribute on his blog, thanks to him I can now explain how I believe in magic during threat modeling.

I have noticed that most of what I do, because it is timeboxed due to carbon based lifeforms constraints, needs to be a finite choice selection from what appears to me as an infinite array of possibilities. I also enjoy pulling computer related magic tricks, or guesses, because it’s amusing and more engaging than reading a checklist. Magic, in this case, is either pure luck or based on some skills the spectators can’t see. I like when I think I’m having both.

During the selection phase of what to do, there’s a few tradeoffs that have been proposed such as coverage, time and skills required. Those are attack based and come from the knowledge of what an attacker can do. While I think that those effectively describe the selection of granular technical efforts, I prefer to look at what are his motivation rather than the constrains he’ll face. And for all that, I have a way or organizing it and showing it.

 

Attack Tree

When I think about the actual threats of a system, I don’t see a list, but rather a tree. That tree has the ultimate goals on top, and then descend into sub-goals that breaks down how you get there. It finally ends up in leaves that are the vulnerabilities to be exploited.

Here’s an unfinished example for an unnamed application:

A fun thing to do with a tree is to apply a weight on a branch. In this case the number represent attacker made tradeoffs and is totally arbitrary.

If you keep it relatively consistent to itself, you end up with an appropriate weighting system. For this example, let’s say it’s the amount of efforts you estimate it takes. You can sum the branches in the tree and get sub-goals weight without having to think about them.

And from that we can get a sum for the root goals:

But then how do I choose to prioritize or just work on something?

I could just say, well I’m going to do the easiest things to do, maybe because finding an SQLi in the application is easier than finding a slow API request, so better start looking at that first.

But regarding to decision, I often decide to do the most common human behavior: just don’t do it myself.

With the help of the tree, I just let the actual business reality do the selection on which root goals to pick. By that I mean the literal definition of reality, although nowadays people seems to forget what it really means:

“reality · noun · 1. the world or the state of things as they actually exist, as opposed to an idealistic or notional idea of them.”
– Google Almighty

I never ask the business line if they think they’ll have SQLi, but rather, if they worry more about denial of service or information stealing.

One advantage of that, is that those decisions are at the root goals. The tree is a hierarchy; the higher level you are, the bigger impact you’ll have. Like spinning a big cog wheel versus a smaller one:

3 gears

If you were to pick on each vulnerability at the time, you’ll spin your working wheel a lot, while just really advancing the root goal a bit. Work on doing the selection on the root goals, then you’ll see that it’s impact is far greater for about the same amount of time. That’s efficiency to me.

And that’s how I turn magic into engineering 😀

Of course, in order for it to be proper engineering, the next step would be to QA it. And at that point, you can fetch all the checklists or threats repository you can find, and verify that you covered everything in your tree. Simply add what you have missed, and then bask in the glory of perceived completeness.

 

For the curious practitioners, I’ve used PlantUML in order to generate the tree examples as seen above. The tool let you textually define the tree using simple markup and auto balance it for you when you are updating it. A more detailed example can be found on my Threat Modeling Toolkit presentation.

Babylonian Triginometry

a fresh look at a 3700-year-old clay tablet suggests that Babylonian mathematicians not only developed the first trig table, beating the Greeks to the punch by more than 1000 years, but that they also figured out an entirely new way to look at the subject. However, other experts on the clay tablet, known as Plimpton 322 (P322), say the new work is speculative at best. (“This ancient Babylonian tablet may contain the first evidence of trigonometry.”)

The paper, “Plimpton 322 is Babylonian exact sexagesimal trigonometry” is short and open access, and also contains this gem:

If this interpretation is correct, then P322 replaces Hipparchus’ ‘table of chords’ as the world’s oldest trigonometric table — but it is additionally unique because of its exact nature, which would make it the world’s only completely accurate trigonometric table. These insights expose an entirely new level of sophistication for OB mathematics.

Celebrating Alt-Left Lawlessness

Lately, I’ve tried to stay away from the tire fire that American politics has become. I’m reasonably certain that I have more to contribute in other areas. But when the President tries to equivocate between those waving the Nazi flag and those protesting against them, we need to speak about what’s acceptable.

It ought to go without saying that when literal Nazis are on one side of a debate, the other side is in the right.

But apparently, that’s not obvious, so I felt I could share a plan for a march by the alt-left, under the ominous name of “Operation Overlord.” They were planning to overthrow the legitimate government all along the coast, and, through force, replace it with their own puppets.

More seriously, we can have disagreements about what’s best for the country, and it’s bad when we demonize those who disagree with us. Civilized society requires us to accept civil disagreement. It accepts that no one is privileged or disadvantaged by an accident of birth: “race, creed or color,” as the expression goes. But civil disagreement, by definition, precludes violence, advocacy of violence or threats of violence.

The Nazi flag is one such threat. Waving it has no purpose except declaring oneself outside society and at odds with the ideals and principles of good people everywhere.

If you’re in a crowd of Nazis, you should be asking why, and walking away.

If you have doubts about what a President should say, here’s a sample:

Amicus brief in “Carpenter” Supreme Court Case

“In an amicus brief filed in the U.S. Supreme Court, leading technology experts represented by the Knight First Amendment Institute at Columbia University argue that the Fourth Amendment should be understood to prohibit the government from accessing location data tracked by cell phone providers — “cell site location information” — without a warrant.”

For more, please see “In Supreme Court Brief, Technologists Warn Against Warrantless Access to Cell Phone Location Data.” [Update: Susan Landau has a great blog post “Phones Move – and So Should the Law” in which she frames the issues at hand.]

I’m pleased to be one of the experts involved.

Learning From npm’s Rough Few Months

The node package manager (npm) is having a bad few months. Let’s look at what we can do, what other package managers should do and what we can learn at a policy level, particularly in the U.S. framing of “critical infrastructure.”

People in security who remain focused on the IT side of the house, rather than the development side, may not be familiar with npm. As its website says, “npm is the package manager for JavaScript and the world’s largest software registry. Discover packages of reusable code — and assemble them in powerful new ways.” Odds are excellent that one or more of your websites rely on npm.

I wrote a long post on the subject at the IANS blog.

The Evolution of Ctenophore Brains

From his very first experiments, he could see that these animals were unrelated to jellyfish. In fact, they were profoundly different from any other animal on Earth.

Moroz reached this conclusion by testing the nerve cells of ctenophores for the neurotransmitters serotonin, dopamine and nitric oxide, chemical messengers considered the universal neural language of all animals. But try as he might, he could not find these molecules. The implications were profound.

Read “Aliens in our midst” at Aeon.

Magical Approaches to Threat Modeling

I was watching a talk recently where the speaker said “STRIDE produces waaaay to many threats! What we really want is a way to quickly get the right threats!”*

He’s right and he’s wrong. There are exactly three ways to get to a short list of the most meaningful threats to a new product, system or service that you’re building. They are:

  • Magically produce the right list
  • Have experts who are so good they never even think about the wrong threat
  • Produce a list that’s too long and prune it

That’s it. (If you see a fourth, please tell me!)

Predictions are hard, especially about the future. It’s hard to know what’s going to go wrong in a system under construction, and its harder when that system changes because of your prediction.

So if we don’t want to rely on Harry Potter waving a wand, getting frustrated, and asking Hermione to create the right list, then we’re left with either trusting experts or over-listing and pruning.

Don’t get me wrong. It would be great to be able to wave a magic wand or otherwise rapidly produce the right list without feeling like you’d done too much work. And if you always produce a short list, then your short list is likely to appear to be right.

Now, you may work in an organization with enough security expertise to execute perfect threat models, but I never have, and none of my clients seem to have that abundance either. (Which may also be a Heisenproblem: no organization with that many experts needs to hire a consultant to help them, except to get all their experts aligned.)

Also I find that when I don’t use a structure, I miss threats. I’ve noticed that I have a recency bias, towards attacks I’ve seen recently, and bias towards “fun” attacks, including spoofing these days because I enjoy solving those. And so I use techniques like STRIDE per element to help structure my analysis.

It may also be that approaches other than STRIDE produce lists that have a higher concentration of interesting threats, for some definition of “interesting.” Fundamentally, there’s a set of tradeoffs you can make. Those tradeoffs include:

  • Time taken
  • Coverage
  • Skill required
  • Consistency
  • Magic pixie dust required

I’m curious, what other tradeoffs have you seen?

Whatever tradeoffs you may make, given a choice between overproduction and underproduction, you probably want to find too many threats, rather than too few. (How do you know what you’re missing?) Some of getting the right number is the skill that comes from experience, and some of it is simply the grindwork of engineering.

(* The quote is not exact, because I aim to follow Warren Buffett’s excellent advice of praise specifically, criticize generally.)

Photo: Magician, by ThaQeLa.