Responding before reading @alissaknight's: Playing with FHIR: Hacking and Securing FHIR APIs

If you've been sitting under a rock, you missed the latest big explosion in the FHIR World, Alissa Knight's most recent report: Playing with FHIR: Hacking and Securing FHIR APIs.  I've downloaded it, and looked at the fantastic graphics (e.g., image right), but I've not really read it yet.  This is the kind of report that will require as deep a read I suspect as some Federal regulations.  If I get the time (perhaps this weekend), I may even do a tweet-through-read-through.

I have some initial thoughts based on my reading of the tweets in this stream:

“(FHIR app ecosystem) contained “pervasive authorization vulnerabilities” that enabled Knight to access > 4M patient and clinician records with just a single patient login account” This is truly frightening. Would love your perspective @motorcycle_guy @amalec @aneeshchopra https://t.co/EOHKNvzQT0

— claudiawilliams (@claudiawilliams) October 13, 2021

Here's what I expect to find, noting that this is only my guess as to what is happening:

Various apps rely on a FHIR backend which will allow a replay attack to be performed whereby:

1. The attacker obtains access to the authorization token used by the FHIR API call to make other API calls on a different patient. 
   
   NOTE: There are a number of ways to obtain this authorization token depending on how the application is constructed and the level of access one has to developer tools, and application hardware and software.  Assume that the hostile attacker is one in a million that has access to all of that, not the common high-school student (but don't count them out either, some of them are that good).  Is ia a Java or .Net app?  There's a debugger for that, and I can almost assuredly final all of your assemblies or jar files on my device emulator, and debug code running in a device.  Did you obfuscate your code?  If not, I can reverse compile it, there are tools for that too, and even obfuscation is not enough when you consider that platform calls are still going to be obvious, and all the important ones are well known, so I can work back the stack to the code I need to manually investigate.
   
   
2. The attacker constructs new API calls to make request using the same authorization token.
   
   
3. The call succeeds because the only check that is performed on the authorization token by the back end server is that it is a valid token issued by the appropriate authorizer.

The fix is not simple.  Basically, the back end developer needs to bind the access control restrictions associated with the original access request to obtain the authorization token to the subset of data that it authorizes access to (this is the easy part), and enforce them on every request (this is the hard part), incoming and outgoing (unless you can prove the outgoing data will match the authorization based on how the query works).  JWT tokens provide more than adequate capability to bind exactly those details in an easily accessible way.  Essentially, the claims in the JWT should indicate what is allowed to be accessed, when, how, and by whom.  Sadly for my first FHIR Server, JWT was still a work in progress, but I simply encoded an index in the token which pointed to memorialized claims in a database, constructed when the token was first created.

Once you can do that much, fine-grained access control is actually quite simple.  That was originally considered to be out of scope in my first effort for reasons of cost and complexity, but because we had to build the right security infrastructure even to support coarse-grained access control, fine-grained control simply became just a little bit harder, but also a worthwhile differentiator from other offerings.

As I said earlier, my guess as to what is wrong is only supposition, an experienced, wild-assed guess at what I will find upon reading.  But if I can guess it before reading, then arguably, so can someone else as smart as I without barely even an incentive.  Consider also that intelligence and quality of character are not necessarily correlated, nor do they even need to exist in the same body for a vulnerability such as I described above to be readily and easily exploited by someone with enough motive, and knowledge about how to use tools that people smarter than them wrote.

In the world of the great world wide web, FHIR API servers, Java, .net or other platform, the basic concepts of servlets, intercepters, filters, hooks, cut points or similar concepts by other names in non-Java platforms all exist and are able to perform these checks and validations both before and after the call completes, so that you can:

1. Verify that what is being asked for is allowed to be asked for (never assume that the querant of your back end can only be your application)
2. The data that is being returned also matches what the authorization allows the end user to see, and either filter, or simply reject the request (after having performed some work that you wish you hadn't).

I led teams building (writing some of the code myself) commercial FHIR servers on top of readily available open source frameworks a number of times in my life (most are still in use), and I've also implemented other API interfaces pre-FHIR.  When I built in security controls, I bound the security tokens to the specifically authorized purpose and sources, and I didn't trust that token to be used only for that purpose, I made sure it was verified every time.

I have to tell you that was also a moderate pain point, because it cost about 10% of my processing time budget. I could however, justify it by citing the specific regulatory text regarding fines for breach, and that I think made a huge difference in my ability to argue for that kind of security (this was perhaps, the hardest part).

I expect to be reading a wakeup call, we'll see how my prediction turns out.  Remember too, APIs are still a work in progress.

Security is hard.

Good Security is harder.

Great Security is an ongoing work in progress.

Perfect security does not exist.

On Air: Part Two

 

Since my initial foray into developing my own little IoT device that will be controlled by my headset that started in On Air: Part 1, I've managed to accomplish three things:

1. Discovered that the battery drain from the light is too much for as often as I'm on a headset so that batteries would bankrupt me (three batteries lasted two days).  Fortunately, there's a USB C power adapter for this fixture.
2. Rewired the power connection so that it was driven by the power to thee on-off switch, rather than power supply from the battery.
3. Written a couple of node.js scripts to turn the beasty on and off from the command line.

In part 3 or 4 I'll open this thing up to show you my (crappy) wiring.

Step 3 was not nearly as hard as I expected, and that's because everything I need to control the device is right here.

The two scripts are dead simple.  Here's the one to turn it on.  The one to turn it off is left as an exercise for the reader.

const ewelink = require('ewelink-api');

/* instantiate class */

const connection = new ewelink({

  email: '█████████@██████',

  password: '███████████',

  region: 'us',

});

async function f1() {

/* turn on/off device */

const device = await connection.setDevicePowerState('██████████', 'on');

console.log(device);

}

f1();

First, create a connection class, and give it your username (which is your e-mail address), your password, and your region.  Mine happens to be 'us'.

Secondly, create an async function that will turn the device on and report its status.  You'll need the device id, fortunately, it is written on a white sticky attached the controller chip of the device, so get your phone or a magnifying glass to read it, and enter it (in quotes) as the first argument to the call to setDevicePowerState().

Finally, call that function.

To run the script, just enter:

node turnOn.js

at the command line, and walla, the light goes on.  Run

node turnOff.js

and it turns off.

Now, to detect whether my headset is in use, and call the right script.  This will also be remarkably easy.  But the devil is in the details.

I suppose I could simply this script so that it took a command line argument, but again, that's an exercise for the reader.

On Air: Part one

My home office has a wide open arch entry way right off the main hallway between the living room and kitchen, and is also the route from kitchen to the upstairs master bathroom which all the people in my house prefer to use for their showers.  My family understands that when they see me with a headset on that I may very likely be on a zoom/webex/teams/pick-your-favorite-teleconfering-app call.  I have two cameras in my office that I might use for these calls, one with a face view, and one with a side view (the laptop camera) due to where my laptop sits.  The side view points to the arch, and I rarely use it, sadly, if the front facing camera gets disconnected, that one is always available and becomes the default camera.

Imagine not being able to, in your own home, wander from your bedroom to the bathroom wrapped in a towel, and having to worry about being on camera in the background.  Not ideal.  I've put a sticky over that camera for now to address this challenge.  But since I'm on calls so much, I often wear my headset most of the day (it also helps notify me of incoming requests for attention).  And so, to avoid the inevitable question "Are you on a call", I thought it might be a good idea to get myself a wirelessly controlled, battery operated on-air light, and then have it be controlled by detecting use of a headset with my computer.

Yeah, this is NOT a commercially available solution, but all the pieces are out there.  You can get an On Air light, a 5V DC operated WiFi switch, and a battery pack to create what I call "On Air: Part one".  The parts below (or similar) are what you need.

Take the back cover off the light, and cut the red wire going from the battery case to the on-off switch.  Route two wires so that they can get out the back (I simply cut a hole in the back of the light), and connect to the newly cut power feed.  Connect the wire from the battery case itself to the center (common) position of the relay using the new wire (routed through the back), and connect the second wire to the on-off switch to the bottom terminal of the relay.  Insulate the newly made connections (I used heat shrink tubing, but electrical or other tape will work).  Connect the new battery pack to the WiFi switch. Install batteries in both battery packs.

Download the App that works with the switch you purchased and follow the directions to connect the relay to the app.  You MAY need to reconfigure your router to separate your 2.4Ghz network from your 5 Ghz network to make everything work (I did).  Test the switch in the manufacturer's selected App.

Tell Google Home about the new switch you installed.  

OK, at this point, you've now got a remote controlled On Air lamp.  In part two, I will show you how to control that from your PC, and in Part three, I'll explain how you can detect use of a headset on your laptop.  I have three different headsets I can use, and one works with both my phone and my laptop, and the other with my iPad and my laptop, and the third with any device I plug in into.  The software solution I put together should work on when any of these devices is used for communication.  I'll leave it to you to guess how I make that work (when I get there).

Stratifying Race and Ethnicity for SANER

Variation is the bane of standards.  Eliminating needless variation is part of my job.  Doing it in a way that doesn't increase provider (or developer) burden is an indication that it's been done right.

I've looked at a lot of state and national dashboards while working on the SANER Project, and one thing I notice is the variation in reporting for data with respect to race and ethnicity classifications (strata).  Often, when reported on publicly, these two different categories are combined into smaller sets, with groupings like multiple race, other and unknown.

ONC National Coordinator Micky Tripathi noted Health IT reporting variation for this kind of data in his keynote delivered at a recent Strategic Health Information Exchange Collaborative (SHIEC) conference.

Federal reporting uses separate fields for race and ethnicity, and allow for multiple values to be reported for race.  There are 5 possible values for race (not counting various flavors of unknown), and two values for ethnicity according to OMB Reporting requirements.

Reporting multiple races means that there are several ways to report none (flavors of null including unknown, refused to answer and did not ask), 5 ways to report one race, 10 to report two races, 10 ways report three races, 5 ways to report four, and 1 way to report all five, resulting in around than 33 categories.  

Combining that with the various ways to report ethnicity (again with flavors of none), that results in about 165 possible reporting categories.  Looking at the actual statistics, there are about 50 categories that would generally be needed for a given facility (e.g., frequency > a few tenths of a percent) to stratify populations according to race and ethnicity, if non-existing groupings are not reported on, and perhaps an even smaller number for smaller facilities.  It wouldn't be possible for example, for a 100 bed hospital to even use all of the category combinations.

The data is generally rolled up into a much smaller number of reporting categories which vary between states, and these often also vary with how federal dashboards report the same data.  Different states have different racial and ethnic makeups and the public reporting at race and ethnicity data at these levels is designed to address potential disparities relevant to the state.

Given that many state departments of health  also support reporting to federal agencies, how does one normalize reporting without having to have 51 separate specifications for reporting?

The best way to handle this is to stratify by the combination of race and ethnicity, and report all possible existing combinations.  In other words, don't report 0 values for combinations that don't exist, as that can be inferred from the data.  This enables states to roll up this data into a smaller set of categories for their public reporting, yet retain the data needed for federal reporting, and enable federal reporting to roll up differently.  When automatically computed, this level of stratification does not introduce a reporting burden on the reporting providers.

Thinking Ahead

A very long time ago, when I worked at Florida State University, I had two rules for programming written on my board:

1. Just get it to work.
2. If it works, don't mess with it.

It became a test of the quality of people who would read it, as the ones who suffered from some sort of reaction while interpreting the ramifications were definitely the people that I wanted to have around me.

If you just get it to work, and don't mess with it, you have something that has the very least effort put into it.  If you need to change it in the future, good luck with that.

Along the same lines, when I was growing up, a kid who lived on my street had a small car, I think it was a Dodge of some sort, and he wanted to beef it up. So he took out the engine and tranny, and replaced it with a MUCH bigger one.  Somehow he managed to make it all fit together after modifying the drive shaft, but he forgot one very important thing: Engine torque.  When he finally started the car after spending the better part of a year on this project, he wound up warping the frame, something like the picture to the right.  The car was a total loss.  The point of this story is that you can only do so much with a limited infrastructure.

One of the challenges with FHIR adoption for some use cases is that there are existing HL7 interfaces, labs, ADT feeds, immunizations, et cetera, that are already widely deployed, adopted, and working, in fact, working well.  There's little desire to replace these interfaces with FHIR based interfaces because:

1. What we have is good enough for what we are doing.
2. It's working right now.

But as we keep pushing the interoperability needle higher and higher, eventually we will have replace these interfaces. When should we do that?

1. When what we have isn't good enough for what we want to do.
2. Or we can't make want we want to do easily work with what we have right now.

The HL7 V2 to FHIR project is an example of what happens when interfaces get stuck in these situations, we cannot easily connect them to newer infrastructure so that we can do more with them, so we build things that enable us to convert from one to the other.  The very existence of the project demonstrates that there's more than we want to be able to with the data present in HL7 Version 2 messages.  This might include things like:

• Aggregating data from multiple sources
• Providing more sophisticated searching capabilities
• Enabling data subscriptions

There's a lot of effort and cost associated with replacing something that works with something else, and it's hard to justify that when the thing that's working is in fact still working.  But, if there was a way to upgrade, replacing your scooter with a Corvette (and you can justify the need for a Corvette), then it might in fact be worthwhile.

When interface standards are mandated by regulatory policy, it's pretty difficult to upgrade.  Consider what happened with X12 5010 standards, or the whole discussion around CCDA 1.1 and CCDA 2.1 backwards compatibility.  It's even more difficult when it all has to happen in a very short time frame.  We need to consider how to have policy enable these kinds of shifts, over REASONABLE time frames.  Two years is not enough time to roll out a new standard without severely impacting an industries capacity to do anything else but roll that out.  We know that from experience (or at least I hope we do).

But what would the next generation ADT, lab, immunization or other standards look like?  And what would they enable us to do that the current ones don't.  It's time to start thinking about that.

YAML as a FHIR Format

 

YAML (short for "YAML Ain't Markup Language" but I simply prefer "Yet Another Markup Language") is a file format that even further simplifies writing structured data files.  A while back I struggled with writing measures for the SANER Project because both XML and JSON formats have some minor issues that make it hard to hand code the expressions.

XML sucks because, well, XML sucks.  Whitespace is really valuable for formatting code, but XML just wants to make it to be gone.

JSON isn't much better because you have to escape newlines and once again, cannot see your code structure for expressions.

I dinked around a bit with YAML input and output, and now because I'm creating a new measure, wanted to get it working properly, which I have now done, at least in so far as my YAMLParser now correctly round trips from JSON to YAML and back to JSON.

The key to making this work is using Jackson to convert between JSON and YAML, and configuring the YAML quotes right so that strings that look like numbers (e.g., "001") don't get treated incorrectly as numbers when converting between the two.

The methods newYAMLMapper() creates a Jackson YAMLMapper correctly configured.

    public static YAMLMapper newYAMLMapper() {
        YAMLMapper m = new YAMLMapper();
        return
          m.enable(YAMLGenerator.Feature.LITERAL_BLOCK_STYLE)
           .disable(YAMLGenerator.Feature.MINIMIZE_QUOTES)
           .disable(YAMLGenerator.Feature.USE_PLATFORM_LINE_BREAKS)
           .disable(YAMLGenerator.Feature.SPLIT_LINES);
    }

Methods for converting between YAML and JSON are fairly simple:

    public static String fromYaml(String yaml) 

      throws JsonMappingException, JsonProcessingException

    {

        ObjectMapper yamlReader = newYAMLMapper();

        Object obj = yamlReader.readValue(yaml, Object.class);

        ObjectMapper jsonWriter = new ObjectMapper();

        return jsonWriter.writeValueAsString(obj);

    }

    public static String toYaml(String jsonString) throws IOException {

        // parse JSON

        JsonNode jsonNodeTree = new ObjectMapper().readTree(jsonString);

        // save it as YAML

        String jsonAsYaml = newYAMLMapper().writeValueAsString(jsonNodeTree);

        return jsonAsYaml;

    }

Converting from streams and readers works similarly.

The YamlParser class implements IParser.  It contains an embedded jsonParser convert resources back and forth between Java classes and JSON formats, and then uses toYaml and fromYaml methods in encodeResourceToString and parseResource methods to read/write in YAML format.  It's NOT the most efficient way to read/write YAML to FHIR, but it works (correctly as best I can tell).

    public static class YamlParser implements IParser {

        private final IParser jsonParser;

        YamlParser(FhirContext context) {

            jsonParser = context.newJsonParser();

        }

        @Override

        public String encodeResourceToString(IBaseResource theResource) 

            throws DataFormatException

        {

            try {

                return toYaml(jsonParser.encodeResourceToString(theResource));

            } catch (IOException e) {

                throw new DataFormatException("Error Converting to YAML", e);

            }

        }

        @Override

        public <T extends IBaseResource> T 

            parseResource(Class<T> theResourceType, InputStream theInputStream)

            throws DataFormatException {

            try {

                return jsonParser.parseResource(

                    theResourceType, fromYaml(theInputStream));

            } catch (IOException e) {

                throw new DataFormatException("Error Converting from YAML", e);

            }

        }

        ...

    }

All of the setter/getter methods on YamlParser delegate the work to the embedded JsonParser, as shown in the examples below.  

        @Override
        public void setEncodeElementsAppliesToChildResourcesOnly(
            boolean theEncodeElementsAppliesToChildResourcesOnly) {
            jsonParser.setEncodeElementsAppliesToChildResourcesOnly(
                theEncodeElementsAppliesToChildResourcesOnly);
        }

        @Override
        public boolean isEncodeElementsAppliesToChildResourcesOnly() {
            return jsonParser.isEncodeElementsAppliesToChildResourcesOnly();
        }

A full blown implementation can be found at YAML Utilities for FHIR 

Help Adrian Get Treatment

I'm asking this at the request of my eldest adult child, going by Aeowolfe these days.  Some of you may have met him a few years back or seen him help me out in some videos for Health IT in the past.

My eldest child's partner has been suffering from chronic pain since mid-2017.  The onset of the disease occurred while they were at College were my eldest met them.  Since then, they lost the support of their family, have subsequently been diagnosed with Fibromyalgia, hypermobility disorder (similar to Ehlers-Danlos syndrome, only missing direct family member diagnosis to match the clinical criteria), and endometriosis.  Our family has been providing support in various ways, including helping them get signed up for Mass Health and disability through Social Security (which took over 3 years to get to).

They've found a treatment plan they think will help (Ketamine-Lydocaine transfusions), but they cannot afford them at their present income, and insurance doesn't yet cover this treatment because it is new (there have been several clinical trials with positive results).  So my eldest has started a gofundme for Adrian.  If you are able and willing to contribute you will have both my thanks, and that of my eldest.