The MirageOS Blogon building functional operating systems


Organized chaos: managing randomness

By David Kaloper

This post gives a bit of background on the Random Number Generator (RNG) in the recent MirageOS v2.5 release.

First we give background about why RNGs are really critical for security. Then we try to clarify the often-confused concepts of "randomness" and "entropy", as used in this context. Finally, we explore the challenges of harvesting good-quality entropy in a unikernel environment.

Playing dice

Security software must play dice.

It must do so to create secrets, for example. Secrets can then serve as the keys that protect communication, like the Diffie-Hellman key exchanged between two TLS endpoints. Proof of the knowledge of a particular secret can be used to verify the identity of someone on the Internet, as in the case of verifying the possession of the secret RSA key associated with an X.509 certificate. As an attacker guessing a secret can have disastrous consequences, it must be chosen in a manner that is realistically unpredictable by anyone else — we need it to be random.

There are other reasons to use randomness. A number of algorithms require a unique value every time they are invoked and badly malfunction when this assumption is violated, with random choice being one way to provide a value likely to be unique. For example, repeating the k-parameter in DSA digital signatures compromises the secret key, while reusing a GCM nonce negates both confidentiality and authenticity. Other algorithms are probabilistic, in that they generate random values before operating on an input, and then store the chosen values in the output, such as the OAEP padding mode for RSA. This is done in order to confuse the relationship between the input and the output and defeat a clever attacker who tries to manipulate the input to gain knowledge about secrets by looking at the output. Still other algorithms pick random numbers to internally change their operation and hide the physical amount of time they need to execute, to avoid revealing information about the secrets they operate on. This is known as blinding, and is one way to counter timing side-channel attacks.

Randomness is therefore quite pervasive in a security context. In fact, many cryptographic algorithms are designed under the assumption of a readily available source of randomness, termed a random oracle. The security analysis of those algorithms is conditional on the oracle; we know that they have certain security characteristics, like the difficulty of guessing the correct message or impersonating somebody, only given an ideal random oracle.

And so security software has a problem here. Computers are inherently deterministic, made to behave reproducibly given a known program and starting state. How to go about solving this?

Random failures

Before taking a look at how we try to solve this problem, let's instead consider what happens if we fail to do so. There is even a Wikipedia page about this, which is a nice starting point. Some of the highlights:

The first public release of Netscape's original SSL, version 2.0, was broken several months after its release. The weakness was in initializing the RNG with the current time, the process ID and the parent process ID of the browser. The time stamp can be guessed to a certain precision, leaving only its sub-second part and the two PIDs unknown. This relatively small unknown space of initial values can be brute-forced.

About a decade later, Debian patched their version of OpenSSL and reduced RNG initialization to the current PID. As a result, only 32767 random sequences were possible. This flaw went undetected for two years and became known as the Debian fiasco. Personal reports indicate that some of the 32767 distinct secret keys that could be generated with OpenSSL on a Debian system during that time are still in circulation.

Computing the largest common divisor of a pair of numbers is much faster than discovering all the prime divisors of a particular number. RSA public keys contain a number, and secret keys contain its factors. An RSA key is usually generated by randomly picking the factors. If a pool of keys was generated with a heavily biased random number generator, such that factors are likely to repeat, it is possible to search for common factors in all pairs and crack the affected keys, a technique which produces spectacular results.

Recently, a bitcoin application for Android was discovered to be downloading its random initial value from a website. It wasn't even necessary to intercept this unencrypted traffic, because the website started serving a redirect page and the Android application was left initializing its RNG with the text of the redirection message. It therefore started generating the same private ECDSA key and the associated bitcoin address for every affected user, an issue which reportedly cost some users their bitcoins.

Playstation 3 game signatures can be forged. Sony reused a single k-parameter, which is supposed to be "unique, unpredictable and secret", for every ECDSA signature they made. This lead to complete compromise of the signing keys. Admittedly, this is not really an RNG problem in itself, but it shows where such a malfunction can lead.

These are only some of the most spectacular failures related to random numbers. For example, it is widely known in security circles that RNGs of embedded devices tend to be predictable, leading to widespread use of weak keys on routers and similar equipment, amongst other things. So when implementing a unikernel operating system, you don't want to end up on that Wikipedia page either.

Random sequences and stuff

But what are random numbers, really? Intuitively, we tend to think about them as somehow "dancing around", or being "jiggly" in a sense. If we have a software component that keeps producing random outputs, these outputs form a sequence, and we hope this to be a random sequence.

But such a thing is notoriously difficult to define. The above linked page opens with the following quote:

A random sequence is a vague notion... in which each term is unpredictable to the uninitiated and whose digits pass a certain number of tests traditional with statisticians.

The intuitive jigglyness is captured by statistical randomness. We require each output, taken independently, to come from the same distribution (and in fact we want it to be the uniform distribution). That is, when we take a long sequence of outputs, we want them to cover the entire range, we want them to cover it evenly, and we want the evenness to increase as the number of outputs increases — which constitutes a purely frequentist definition of randomness. In addition, we want the absence of clear patterns between outputs. We don't want the sequence to look like 7, 8, 9, 10, ..., even with a bit of noise, and we don't want correlation between outputs. The problem here is that no-one really knows what "having patterns" means; it is entirely possible that we only searched for patterns too simple, and that in fact there is a pattern that fully explains the sequence lurking just around the complexity corner.

Nonetheless, there is a well established battery of tests to check statistical randomness of RNG outputs, called the Diehard Tests, and serves as the de-facto standard for testing random number generators. Here's the beginning of a certain sequence that passes the test with flying colors:

3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5, 8, 9, 7, 9, 3, 2, 3, 8, 4, 6, 2, 6, 4, 3, 3, 8, 3, ...

We still would not recommend using digits of π as a secret key. Neither would we recommend releasing software for everyone to study, which uses that sequence to generate the secrets. But what went wrong?

The other concept of randomness. Roughly, a random sequence should not be predictable to anyone with any knowledge other than the sequence itself. In other words, it cannot be compressed no matter how much we try, and in the extreme, this means that it cannot be generated by a program. While the latter restriction is obviously a little too strong for our purpose, it highlights a deep distinction in what people mean by being "random". Jumping around is one thing. Being actually unpredictable is a wholly different matter.

There are many other simple mathematical processes which generate sequences with high statistical randomness. Many of those are used to produce "random" sequences for various purposes. But these are still completely deterministic processes that exhibit random behaviour only in the statistical sense. Instead of being random, they are pseudo-random, and we call such generators Pseudo-Random Number Generators (PRNGs).

We can look for something approaching a "true" random sequence in nature. The current agreement is that the nature of quantum processes is random in this sense, and random sequences based on this idea are readily available for download. Or we can use the microphone and keep recording; the lowest-order bits of the signal are pretty unpredictable. But we cannot write a program to generate an actually random sequence.

Still, we need to compromise. The real problem of common PRNGs is that knowing the rule and observing some of the outputs is enough to predict the rest of the sequence. The entire future behavior of Mersenne twister, one of the most commonly used generators in various programming packages, can be predicted after observing only 624 outputs in a row. A step up from such a process is a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG). Their key property is that it is computationally prohibitively expensive to distinguish their outputs from a "true" random sequence. This also means that it is computationally prohibitively expensive to reconstruct their internal state, just by looking at their outputs. In a sense, someone trying to predict the outputs can not take shortcuts, and is instead forced to perform the laborious task of starting the generator with all the possible states and checking if the output matches the observed sequence. This is how we can quantify a CSPRNG unpredictability: it takes trying about half of all the possibilities to guess the state.

MirageOS' security stack contains a CSPRNG, a design called Fortuna. What it really does, is encrypt the simple sequence 0, 1, 2, 3, ... with AES (AES-CTR) using a secret key. This makes it as resistant to prediction as AES is to known-plaintext attacks. After each output, it generates a bit more, hashes that, and uses the result as the next key. This is not to improve the statistical randomness, as it is already guaranteed by AES. Rather, it's a form of forward secrecy: an attacker who learns the secret key at some point would need to perform the preimage attack on the hash function to figure out the earlier key and reconstruct the earlier outputs.


Although resistant to prediction based solely on the outputs, just like any other software RNG, Fortuna is still just a deterministic PRNG. Its entire output is as unpredictable as its initial value, which we call the seed. From the information perspective, a PRNG can only transform what was unpredictable about its initial seed into an equally unpredictable sequence. In other words, we typically use PRNGs to stretch the unpredictability inherent in the initial seed into an infinite stream. The best PRNGs do not give out more hints about their starting position, but they can never out-race the amount of unpredictability that they started with.

We often call this quality of unpredictability entropy. In a sense, by employing an algorithmic generator, we have just shifted the burden of being unpredictable to the beginning. But now we're cornered and have to search for entropy in the only place where a computer can find it: in the physical world.

A typical (kernel-level) RNG-system reaches out into the world around it through hardware interaction: as hardware events happen, various drivers tend to emit small packets of data, such as the time, or hardware-specific state. These events are a product of the user interactions with the keyboard and mouse, of network packets arriving at an interface, of the hard drive asserting interrupts to signal the end of a DMA transfer, and the like. They are combined together and used to seed the internal (CS-)PRNG.

In fact, describing them as a seed from which the entire sequence is unfolded is a deliberate oversimplification: what really happens is that the PRNG is continuously fed with random events, which change its state as they arrive, and the requests for random bytes are served from the PRNG. The PRNG is used to "mix" the unpredictability inherent in its input, that is, to smooth out various timestamps and similar values into a statistically well-behaved sequence.

Do Virtual Machines Dream of Electric Sheep?

Our problem here is that a virtual machine (VM) in a typical configuration barely sees any physical hardware. Users do not interact with VMs in server scenarios using a directly-connected keyboard and mouse. VMs make use of a virtualized network interface and virtualized disks. Even the CPU features can be intercepted and virtualized. Virtual environments are entropy-starved.

This is a known problem and various analyses of the weakness of random outputs in virtual environments have been published. The problem is especially severe right after boot. The gradual trickle of unpredictability from hardware events slowly moves the pseudo-random stream into an increasingly unpredictable state, but at the very start, it still tends to be fairly predictable. Typically, operating systems store some of their PRNG output on shutdown and use it to quickly reseed their PRNG on the next boot, in order to reuse whatever entropy was contained in its state. Unfortunately, it is common to boot several machines from the same system image, or from a pristine image lacking a seed, making random outputs in a virtual machine vulnerable to prediction close to the startup phase.

To help solve these problems, we employ several sources of entropy in MirageOS unikernels. The case of a Unix executable is simple, as we reuse the system's own RNG, as exposed via /dev/urandom, as the source of our entropy. This is because the kernel is in a much better position to enter an unpredictable state than any single process running under its supervision. The case of Xen unikernels is harder. Here, we group the entropy sources into those that originate within the unikernel itself, and those that originate externally.

In the external case, we again rely on the kernel interacting with the hardware, but this time it's the dom0 kernel. We have a background service, Xentropyd, which runs in dom0, reads the RNG and serves its output to other domains through the Xen Console. The problem is that in many scenarios, like hosting on popular cloud providers, we cannot expect this degree of cooperation from dom0. A bigger problem is that although most of the code is present, we haven't fully fleshed out this design and it remains disabled in MirageOS 2.5.0

So we need to be able to achieve unpredictability relying purely on what is available inside a unikernel. A unikernel has no direct exposure to the hardware, but it is of course interacting with the outside world. To tap into this ambient entropy, we have to continuously sample all inter-event timings in its event loop. This process is analogous to what happens in a full-blown OS kernel, except our events lack the extra hardware context, and our timers are potentially less granular (for example, on ARM). This makes our interaction-based events somewhat more predictable, or in other words, they have a little less entropy.

Recent Intel chips come with an on-die random generator, which ultimately derives from thermal readings, and is available through RDRAND and (more directly) RDSEED instructions. The community has expressed concern that relying exclusively on this generator might not be a wise choice: it could silently malfunction, and its design is hidden in the hardware, which raises concerns about potential intentional biases in the output — a scheme not unheard of. However, since entropy is additive, its output can never reduce whatever unpredictability the system already has. Therefore, if available, we continuously sample this on-die RNG, and inject its outputs into our PRNG.

The combination of event timings and a built-in RNG does have good unpredictability in the long run, especially if our unikernel is running on a multi-tenant host and competing for CPU with other instances. But the entropy in each individual event is still relatively low: we can assume that a determined attacker can guess each individual time stamp up to a certain precision that we don't know, but which is potentially quite high. This creates the following problem: imagine that an attacker knows the current PRNG state, and can measure the time of the next event, but not with sufficient precision to know the last two bits of the timestamp. To this attacker, our event contains two bits of entropy. If we immediately update the PRNG, the attacker only has to observe some of the output and check four candidate states against it, to fully recover knowledge about the state and negate our entropy addition. On the other hand, if we decide to wait and try to accumulate many more events before updating the PRNG, we keep generating a fully predictable sequence in the meantime.

And here is where Fortuna really shines. It keeps accumulating events in a number of internal pools in a round-robin fashion. These pools are constantly being activated, but with an exponentially decreasing frequency. The pools activated too frequently are wasted, but one of them is activated with just the right frequency to contain enough entropy to make it prohibitively expensive for an attacker to enumerate all the possibilities. This design was shown to be within a constant factor from optimal entropy use, and in particular, scales robustly with the actual amount of entropy inherent in the input events.

This leaves us with the problem of boot-time entropy. Not only can the saved random seed be reused by cloning the disk image, but in many cases, a MirageOS unikernel is running without such storage at all!

Following the design of Whirlwind RNG, we employ an entropy bootstrapping loop. It's an iterated computation, which measures the time it took to perform the previous iteration, and then performs the amount of work that depends on the time, many times over. In this way, it creates a feedback loop with a fragile dependency on any non-determinism in the physical execution on the CPU, such as any contention or races in the CPU state. Even on ARM, which currently uses a less fine-grained timer and whose design is not as parallel as Intel's, this yields an initial value which varies wildly between boots. We use this value to kickstart the PRNG, giving it quick divergence, and ensuring that the state is unpredictable from the very start.

Parting words

While some of our techniques (in particular bootstrapping on ARM) need a little more exposure before we place our full confidence in them — and users should probably avoid generating long-term private keys in unikernels running on bare Xen just yet — the combination of boostrapping, continuous reseeding, and robust accumulation gives us a hopefully comprehensive solution to generating randomness in a unikernel environment.

We intend to re-evaluate the effectiveness of this design after getting some experience with how it works in the wild. To this end, we particularly appreciate the community feedback and you can reach us through our mailing list, or hop onto freenode and join #mirage.

Thanks to Daniel, Mort and Amir for their comments on earlier drafts.