This document describes how we test the quality of the entropy sources used to seed the Zircon CPRNG.
Approximately speaking, it's sometimes easy to tell that a stream of numbers is not random by recognizing a pattern in it. It's impossible to be sure that the numbers are truly random. The state of the art seems to be running several statistical tests on the data, and hoping to detect any exploitable weaknesses.
The problem of testing for randomness gets more difficult when the random numbers aren't perfectly random (when their distributions aren't uniform, or when there are some limited correlations between numbers in the sequence). A stream of non-perfect random numbers still contains some randomness, but it's hard to determine how random it is.
For our purposes, a good measure of how much randomness is contained in a stream of non-perfectly random numbers is the min-entropy. This is related to the Shannon entropy used in information theory, but is always takes a smaller value. The min-entropy controls how much randomness we can reliably extract from the entropy source; see, for example https://en.wikipedia.org/wiki/Randomness_extractor#Formal_definition_of_extractors
From a practical standpoint, we can use the test suite described in US NIST SP800-90B to analyze samples of random from an entropy source. A prototype implementation for the tests is available from https://github.com/usnistgov/SP800-90B_EntropyAssessment. The suite takes a sample data file (say, 1MB of random bytes) as input. The nice thing about this test suite is that it can handle non-perfect RNGs, and it reports an estimate for how much min-entropy is contained in each byte of the random data stream.
The importance of testing unprocessed data
After drawing entropy from our entropy sources, we will mix it into the CPRNG in a "safe" way that basically gets rid of detectable correlations and distributional imperfections in the raw random byte stream from the entropy source. This is a very important thing to do when actually generating random numbers to use, but we must avoid this mixing and processing phase when testing the entropy source itself.
For a stark example of why it's important to test unprocessed data if we want to test our actual entropy sources, here's an experiment. It should run on any modern linux system with OpenSSL installed.
head -c 1000000 /dev/zero >zero.bin openssl enc -aes-256-ctr -in zero.bin -out random.bin -nosalt -k "password"
This takes one million bytes from /dev/zero, encrypts them via AES-256, with a weak password and no salt (a terrible crypto scheme, of course!). The fact that the output looks like good random data is a sign that AES is working as intended, but this demonstrates the risk of estimating entropy content from processed data: together, /dev/zero and "password" provide ~0 bits of entropy, but our tests are way more optimistic about the resulting data!
For a more concrete Zircon-related example, consider jitterentropy (the RNG discussed here: http://www.chronox.de/jent/doc/CPU-Jitter-NPTRNG.html). Jitterentropy draws entropy from variations in CPU timing. The unprocessed data are how long it took to run a certain block of CPU- and memory-intensive code (in nanoseconds). Naturally, these time data are not perfectly random: there's an average value that they center around, with some fluctuations. Each individual data sample might be several bits (e.g. a 64-bit integer) but only contribute 1 bit or less of min-entropy.
The full jitterentropy RNG code takes several raw time data samples and
processes them into a single random output (by shifting through a LFSR, among
other things). If we test the processed output, we're seeing apparent randomness
both from the actual timing variations and from the LFSR. We want to focus on
just the timing variation, so we should test the raw time samples. Note that
jitterentropy's built-in processing can be turned on and off via the
Quality test implementation
As mentioned above, the NIST test suite takes a file full of random bytes as input. We collect those bytes on a Zircon system (possibly with a thin Fuchsia layer on top), then usually export them to a more capable workstation to run the test suite.
Some of our entropy sources are read during boot, before userspace is started.
To test these entropy sources in a realistic environment, we run the tests
during boot. The relevant code is in
kernel/lib/crypto/entropy/quality\_test.cpp, but the basic idea is that the
kernel allocates a large static buffer to hold test data during early boot
(before the VMM is up, so before it's possible to allocate a VMO). Later on, the
data is copied into a VMO, and the VMO is passed to userboot and devmgr, where
it's presented as a pseudo-file at
apps can read this file and export the data (by copying to persistent storage or
using the network, for example).
In theory, you should be able to build Zircon with entropy collector testing
scripts/entropy-test/make-parallel, and then you should be able
to run a single boot-time test with the script
run-boot-test script is mostly
intended to be invoked by other scripts, so it's a little bit rough around the
edges (for example, most of its arguments are passed via command line options
-a x86-64, but many of these "options" are in fact mandatory).
run-boot-test script succeeds, it should produce two files in the
first is the raw data collected from the entropy source, and the second is a
simple text file, where each line is a key-value pair. The keys are single words
/[a-zA-Z0-9_-]+/, and the values are separated by whitespace matching
/[ \t]+/. This file can be pretty easily parsed via
read in Bash,
str.split() in Python, or (with the usual caution about buffer overruns)
scanf in C.
In practice, I'm nervous about bit-rot in these scripts, so the next couple sections document what the scripts are supposed to do, to make it easier to run the tests manually or fix the scripts if/when they break.
Boot-time tests: building
Since the boot-time entropy test requires that a large block of memory be
permanently reserved (for the temporary, pre-VMM buffer), we don't usually build
the entropy test mode into the kernel. The tests are enabled by passing the
ENABLE_ENTROPY_COLLECTOR_TEST flag at build time, e.g. by adding the line
EXTERNAL_DEFINES += ENABLE_ENTROPY_COLLECTOR_TEST=1
local.mk. Currently, there's also a build-time constant,
ENTROPY_COLLECTOR_TEST_MAXLEN, which (if provided) is the size of the
statically allocated buffer. The default value if unspecified is 1MiB.
Boot-time tests: configuring
The boot-time tests are controlled via kernel cmdlines. The relevant cmdlines
kernel.entropy-test.*, documented in
Some entropy sources, notably jitterentropy, have parameter values that can be tweaked via kernel cmdline. Again, see kernel_cmdline.md for further details.
Boot-time tests: running
The boot-time tests will run automatically during boot, as long as the correct kernel cmdlines are passed (if there are problems with the cmdlines, error messages will be printed instead). The tests run just before the first stage of RNG seeding, which happens at LK_INIT_LEVEL_PLATFORM_EARLY, shortly before the heap the VMM are brought up. If running a large test, boot will often slow down noticeably. For example, collecting 128kB of data from jitterentropy on rpi3 can take around a minute, depending on the parameter values.
TODO(SEC-29): discuss actual user-mode test process
Current rough ideas: only the kernel can trigger hwrng reads. To test,
userspace issues a kernel command (e.g.
k hwrng test), with some arguments to
specify the test source and length. The kernel collects random bytes into the
existing VMO-backed pseudo-file at
that this is safely writeable. Currently unimplemented; blocked by lack of a
userspace HWRNG driver. Can test the VMO-rewriting mechanism first.
Test data export
Test data is saved in
/boot/kernel/debug/entropy.bin in the Zircon system
under test. So far I've usually exported the data file manually via
Other options include
scp if you build with the correct Fuchsia packages, or
saving to persistent storage.
Running the NIST test suite
Note: the NIST tests aren't actually mirrored in Fuchsia yet. Today, you need to clone the tests from the repo at https://github.com/usnistgov/SP800-90B_EntropyAssessment.
The NIST test suite has three entry points (as of the version committed on Oct.
restart.py. The two "main"
scripts perform the bulk of the work. The
iid_main.py script is meant for
entropy sources that produce independent, identically distributed data samples.
Most of the testing is to validate the iid condition. Many entropy sources will
not be iid, so the
noniid_main.py test implements several entropy estimators
that don't require iid data.
Note that the test binaries from the NIST repo are Python scripts without a
shebang line, so you probably need to explicitly call
python on the command
line when invoking them.
The first two scripts take two arguments, both mandatory: the data file to read,
and the number of significant bits per sample (if less than 8, only the low
bits will be used from each byte). They optionally accept a
-v flag to produce
verbose output or
-h for help.
noniid_main.py also optionally accepts a
-u <int> flag that can reduce
the number of bits below the
N value passed in the second mandatory argument.
I'm not entirely sure why this flag is provided; it seems functionally
redundant, but passing it does change the verbose output slightly. My best guess
is that this is provided because the noniid Markov test only works on samples of
at most 6 bits, so 7- or 8-bit datasets will be reduced to their low 6 bits for
this test. In contrast, all the iid tests can run on 8-bit samples.
A sample invocation of the
python2 -- $FUCHSIA_DIR/third_party/sp800-90b-entropy-assessment/iid_main.py -v /path/to/datafile.bin 8
restart.py script takes the same two arguments, plus a third argument: the
min-entropy estimate returned by a previous run of
noniid_main.py. This document doesn't describe restart tests. For now, see
NIST SP800-90B for more details.
It would be nice to automate the process of building, configuring, and running a quality test. As a first step, it should be easy to write a shell script to perform these steps. Even better would be to use the testing infrastructure to run entropy collector quality tests this automatically, mostly to reduce bit-rot in the test code. Failing automation, we have to rely on humans to periodically run the tests (or to fix the tests when they break).