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Full disclosure: I am a security product testing nerd*.

 

I’ve been following the MITRE ATT&CK Framework for a while, and this week the results were released of the most recent evaluation using APT29 otherwise known as COZY BEAR.

First, here’s a snapshot of the Trend eval results as I understand them (rounded down):

91.79% on overall detection.  That’s in the top 2 of 21.

91.04% without config changes.  The test allows for config changes after the start – that wasn’t required to achieve the high overall results.

107 Telemetry.  That’s very high.  Capturing events is good.  Not capturing them is not-good.

28 Alerts.  That’s in the middle, where it should be.  Not too noisy, not too quiet.  Telemetry I feel is critical whereas alerting is configurable, but only on detections and telemetry.

 

So our Apex One product ran into a mean and ruthless bear and came away healthy.  But that summary is a simplification and doesn’t capture all the nuance to the testing.  Below are my takeaways for you of what the MITRE ATT&CK Framework is, and how to go about interpreting the results.

 

Takeaway #1 – ATT&CK is Scenario Based

The MITRE ATT&CK Framework is intriguing to me as it mixes real world attack methods by specific adversaries with a model for detection for use by SOCs and product makers.  The ATT&CK Framework Evaluations do this but in a lab environment to assess how security products would likely handle an attack by that adversary and their usual methods.  There had always been a clear divide between pen testing and lab testing and ATT&CK was kind of mixing both.  COZY BEAR is super interesting because those attacks were widely known for being quite sophisticated and being state-sponsored, and targeted the White House and US Democratic Party.  COZY BEAR and its family of derivatives use backdoors, droppers, obfuscation, and careful exfiltration.

 

Takeaway #2 – Look At All The Threat Group Evals For The Best Picture

I see the tradeoffs as ATT&CK evals are only looking at that one scenario, but that scenario is very reality based and with enough evals across enough scenarios a narrative is there to better understand a product.  Trend did great on the most recently released APT/29/COZY BEAR evaluation, but my point is that a product is only as good as all the evaluations. I always advised Magic Quadrant or NSS Value Map readers to look at older versions in order to paint a picture over time of what trajectory a product had.

 

Takeaway #3 – It’s Detection Focused (Only)

The APT29 test like most Att&ck evals is testing detection, not prevention nor other parts of products (e.g. support).  The downside is that a product’s ability to block the attacks isn’t evaluated, at least not yet.  In fact blocking functions have to be disabled for parts of the test to be done.  I get that – you can’t test the upstairs alarm with the attack dog roaming the downstairs.  Starting with poor detection never ends well, so the test methodology seems to be focused on ”if you can detect it you can block it”.  Some pen tests are criticized that a specific scenario isn’t realistic because A would stop it before B could ever occur.  IPS signature writers everywhere should nod in agreement on that one. I support MITRE on how they constructed the methodology because there has to be limitations and scope on every lab test, but readers too need to understand those limitations and scopes.  I believe that the next round of tests will include protection (blocking) as well, so that is cool.

 

Takeaway #4 – Choose Your Own Weather Forecast

Att&ck is no magazine style review.  There is no final grade or comparison of products.  To fully embrace Att&ck imagine being provided dozens of very sound yet complex meteorological measurements and being left to decide on what the weather will be. Or have vendors carpet bomb you with press releases of their interpretations.  I’ve been deep into the numbers of the latest eval scores and when looking at some of the blogs and press releases out there they almost had me convinced they did well even when I read the data at hand showing they didn’t.  I guess a less jaded view is that the results can be interpreted in many ways, some of them quite creative.  It brings to mind the great quote from the Lockpicking Lawyer review “the threat model does not include an attacker with a screwdriver”.

 

Josh Zelonis at Forrester provides a great example of the level of work required to parse the test outcomes, and he provides extended analysis on Github here that is easier on the eyes than the above.  Even that great work product requires the context of what the categories mean.  I understand that MITRE is taking the stance of “we do the tests, you interpret the data” in order to pick fewer fights and accommodate different use cases and SOC workflows, but that is a lot to put on buyers. I repeat: there’s a lot of nuance in the terms and test report categories.

 

If, in the absence of Josh’s work, if I have to pick one metric Detection Rate is likely the best one.  Note that Detection rate isn’t 100% for any product in the APT29 test, because of the meaning of that metric.  The best secondary metrics I like are Techniques and Telemetry.  Tactics sounds like a good thing, but in the framework it is lesser than Techniques, as Tactics are generalized bad things (“Something moving outside!”) and Techniques are more specific detections (“Healthy adult male Lion seen outside door”), so a higher score in Techniques combined with a low score in Tactics is a good thing.  Telemetry scoring is, to me, best right in the middle.  Not too many alerts (noisy/fatiguing) and not too few (“about that lion I saw 5 minutes ago”).

 

Here’s an example of the interpretations that are valuable to me.  Looking at the Trend Micro eval source page here I get info on detections in the steps, or how many of the 134 total steps in the test were detected.  I’ll start by excluding any human involvement and exclude the MSSP detections and look at unassisted only.  But the numbers are spread across all 20 test steps, so I’ll use Josh’s spreadsheet shows 115 of 134 steps visible, or 85.82%.  I do some averaging on the visibility scores across all the products evaluated and that is 66.63%, which is almost 30% less.  Besides the lesson that the data needs gathering and interpretation, it highlights that no product spotted 100% across all steps and the spread was wide. I’ll now look at the impact of human involvement add in the MSSP detections and the Trend number goes to 91%.  Much clinking of glasses heard from the endpoint dev team.  But if I’m not using an MSSP service that… you see my point about context/use-case/workflow.  There’s effectively some double counting (i.e. a penalty, so that when removing MSSP it inordinately drops the detection ) of the MSSP factor when removing it in the analyses, but I’ll leave that to a future post.  There’s no shortage of fodder for security testing nerds.

 

Takeaway #5 – Data Is Always Good

Security test nerdery aside, this eval is a great thing and the data from it is very valuable.  Having this kind of evaluation makes security products and the uses we put them to better.  So dig into ATT&CK and read it considering not just product evaluations but how your organization’s framework for detecting and processing attacks maps to the various threat campaigns. We’ll no doubt have more posts on APT29 and upcoming evals.

 

*I was a Common Criteria tester in a place that also ran a FIPS 140-2 lab.  Did you know that at Level 4 of FIPS a freezer is used as an exploit attempt? I even dipped my toe into the arcane area of Formal Methods using the GYPSY methodology and ran from it screaming “X just equals X!  We don’t need to prove that!”. The deepest testing rathole I can recall was doing a portability test of the Orange Book B1 rating for MVS RACF when using logical partitions. I’m never getting those months of my life back. I’ve been pretty active in interacting with most security testing labs like NSS and ICSA and their schemes (that’s not a pejorative, but testing nerds like to use British usages to sound more learned) for decades because I thought it was important to understand the scope and limits of testing before accepting it in any product buying decisions. If you want to make Common Criteria nerds laugh point out something bad that has happened and just say “that’s not bad, it was just mistakenly put in scope”, and that will then upset the FIPS testers because a crypto boundary is a very real thing and not something real testers joke about.  And yes, Common Criteria is the MySpace of tests.

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