NeuG USB True Random Number Generator
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The problems they're supposed to solve are entirely fictional and usually stem from a poor understanding of the real problems there are with random numbers (which can mostly be traced back to a few issues: boot time entropy, userspace RNGs and use of insecure RNGs by mistake).
Good one; how often has encryption (of any sort) been broken due to not enough randomness? I mean I'm a noob but I can imagine that it'd only become a risk over communications that were tracked for a very long time. or something.
There are no known issues with running out of randomness over time. The main reason to re-seed a CSPRNG is as a defense-in-depth against implementation flaws leaking state.
Also, it's not exactly unheard of of "good" prngs having bugs making them bad.
We hear about "good" CSPRNGs going "bad" all the time, but invariably these are instances of people adding new userland CSPRNGs to their systems as rubber chicken security measures that later blow up.
Yes, I have to place trust in the hardware random generator instead. From a user perspective it seems like the attack surface (from the perspective of bad configuration, start seed, misuse etc.) is much smaller with a hardware generator.
And, conceptually it sounds like a safer option. Is it? Well, we arrive at yet another "I don't know".
Note, I'm not advocating the use of a hardware random generator at all, I'm merely responding to the statement that they don't solve any problem at all.
Keep in mind that the issue was present in Windows 2000 yet remained undetected for 7 years after the release of Windows 2000.
I'm very comfortable with the validity of the argument I'm making, which is that we don't generally need to layer rube goldberg hardware onto our systems to hedge against the likelihood of the LRNG going bad.
And I'd argue that getting good seed values is one of the main things that is appealing with a hardware generator (especially at system boot).
You'd still have to use it though.
And, again, Debian's problem was in their version of the OpenSSL userland RNG. Applications that simply relied on /dev/urandom rather than OpenSSL's janky mess weren't impacted by the bug at all.
https://www.schneier.com/blog/archives/2008/05/random_number...
I think RDRAND is well worth the negligible cost of including it. It can give you entropy immediately, even during early boot (e.g. for ASLR); no need to wait or have any logic to deal with entropy not being ready yet (at least if you can rely on it being available, which should be true in the future). And compared to other approaches, it’s a lot harder to screw up or misuse, eliminating a whole class of potential vulnerabilities (especially if you use it directly rather than just seeding a kernel PRNG with it).
[1] http://forums.xilinx.com/xlnx/attachments/xlnx/EDK/27322/1/H...
That's fine, but boot-time seeding problems are also a real source of RNG-related security problems, as described in the "Mining Your Ps and Qs" paper. (E.g. "The removal of IRQs as an entropy source has likely exacerbated RNG problems in headless and embedded devices, which often lack human input devices, disks, and multiple cores. If they do, the only source of entropy—if there are any at all—may be the time of boot.") Having a hardware TRNG for seeding in this case would presumably prevent these problems.
And it's not a theoretical problem either.
However, if you're that concerned about it, there are open source designs which create free truly random bits, so you are free to choose a design that meets your need.
It's also possible to have MacroFab https://macrofab.com/ manufacture these for you on demand, using the KiCad files: http://git.gniibe.org/gitweb/?p=gnuk/gnuk.git The cost for a single unit is about $45, and it drops quickly to around $30 if you order a few.
The NitroKey Start has the same STM32F103TB (IIRC) and is more readily available:
https://github.com/Nitrokey/nitrokey-start-firmware
https://shop.nitrokey.com/shop/product/nitrokey-start-6
Not sure if an RNG is accessible with the standard firmware (though the NeuG RNG is part of the gnuk firmware).
Buy an ST103F development board ($1.72 shipped to US from China): https://www.aliexpress.com/item/1pcs-STM32F103C8T6-ARM-STM32...
Also buy an STLink programmer for it ($1.98 shipped): https://www.aliexpress.com/item/ST-Link-V2-Programming-Unit-...
Build https://github.com/texane/stlink/ or install OpenOCD.
Clone the NeuG repo and build for the Maple Mini target: https://anonscm.debian.org/cgit/gnuk/gnuk/neug.git/
Now flash neug.bin to the development board via the STLink, then unplug it and plug the board into your nearest micro-USB cable.
You now have a hardware RNG running the same NeuG software on the same processor family as the FST-01.
This board doesn't have enough program space to run GnuK, I think. Nor is it in the tiny form factor of the FST-01 that makes it more suitable as a hardware token.
* software has /no/ random failures, hardware does
* hardware ages and breaks down, often in subtle ways
* a broken down randomness engine is very hard to detect, since all HWRNGs do some whitening: your randomness source can output 1's all the livelong day, when you only see the output of some stream cipher operation or some chained hashes, no statistic tests are going to save you
* probably many more things
Hm, I really should consolidate my notes into that long-planned HWRNG article someday.
Probably the safest thing to do is to use a combination of this and other sources (multiple entropy sources), just in case there's an NSA backdoor or it's otherwise broken. Things like microphones, cameras, input devices, and other sensors are nearly ubiquitous now.
Somewhat philosophical corollary to that is that when you have hash function that is secure enough for your application you can just build CSPRNG from it and use small (eg. 128 or 256 bit) seed and you have no need for hardware RNG.
(Zvi Gutterman refers to the property I'm talking about as "backwards security", so my abuse of the term is particularly egregious).
https://en.wikipedia.org/wiki/Randomness_extractor
...the easiest is the von Neumann extractor where you just extract a zero if both randomness sources are zero, do the same thing for ones and discard all the differences. Half the output with more entropy than any single connected source (if they can't read or influence each other).
I thought the simple solution was to xor the two sources, which is at least a secure the best source?
I don't know the first thing about cryptanalysis, but what I gather from skimming the PDF is that yes, it's possible to advantageously influence a PRNG with bad inputs if the mixer sucks.
(Now would be a great time to plug Matasano...)
Edit: Of course, this introduces the assumption that your hash function is secure, but again, not a problem in practice.
Just adding in more is not always good. You need to trust every source. Or more precisely: You need at least one source in the mix that an attacker can't predict/read out.
This is only true if the attacker knows A, B, and C, in which case you have already lost.
> Just adding in more is not always good. You need to trust every source. Or more precisely: You need at least one source in the mix that an attacker can't predict/read out.
You sort of contradict yourself here. The fact that you need a source that the attacker cannot predict is a reason why it is good to just add more. The more sources you have, the more likely there will be one that the attacker can't predict.
There is also another more pressing issue, which is that if an attacker can see all of your RNG inputs, than they can predict your RNG output anyway, and they can predict your keys anyway. This renders Dan Bernstein's attack against DSA/ECDSA mute.
Of course, the malicious device will also be able to see
other sensitive information, not just x and y. But this
doesn't mean that it's cheap for the attacker to exfiltrate
this information! The attacker needs to find a communication
channel out of the spying device. Randomness generation
influenced by the device is a particularly attractive choice
of channel, as I'll explain below
The point isn't that continuously drawing entropy is necessarily worse. The point is to illustrate that it's not as risk-free as we may think. Furthermore, it doesn't provide much, if any value. In other words, why bother with the added complexity?
And there is added complexity. Linux[1] and OpenBSD use ad hoc mixing schemes for injecting entropy into their PRNGs, with no proof of security. There are schemes with proofs of security, but they're more complex. More over, the ones with the strongest proofs are the most complex of all and often relatively slow, which is why Linux and OpenBSD have been resistant to simply using, e.g., Fortuna.
Similarly, if you give up on constantly trying to inject data, then multiple CPU implementations are easier--just keep a simple PRNG state per CPU, without any fancy waterfall schemes.
These days collecting, even without a hardware RNG, 128 bits of entropy should happen in very short order. Estimates are of no matter, you either have it or you don't, and there's no way to prompt a user, "Do you really want to proceed?" If you can't collect 128 bits of entropy by the time /bin/init loads, you're probably screwed. The best example is a VM--if you have no access to a TRNG or to the host's entropy source, you shouldn't expect the situation to appreciably improve 10 seconds or even 10 minutes later. At the very least, you should assume you're screwed and that the environment isn't a place where you should put services and assets that depend, directly or indirectly, on secure entropy generation.
The logic is, why? Why bother? Especially in security, we normally shouldn't be doing things without well-quantified justifications, especially considering that engineers invariably underestimate the cost of complexity, particularly in the security domain. History has shown that not only do these behaviors create a false sense of security (they're often inadequate in ways we never initially understand[2]), but they can make matters much worse.
[1] At least I think it still does.
[2] No coincidence when the reason for adding them is to solve some ill-defined and poorly understood problem. If you can't quantify the solution you should be wary of relying on it. If you can neither quantify the problem nor the value of the solution (as in the case with constantly adding entropy), it's arguably poor judgment to apply the solution at all.
Modern PRNG frameworks moved away from weighting years ago[1] because it not only has dubious benefit, but engineers suck at estimating entropy, so the estimates created a false sense of security. For example, the entropy estimates for hard drives were for a long time based on a single research publication, which as hard drives evolved over the years became increasingly irrelevant--assuming it wasn't irrelevant the day after it was published. Kernel engineers would try to add conservative margins, but if the original quantity has no basis in reality those efforts are just hand-waving.
This is pretty much how all entropy estimates go. The research that goes into the estimates can be extremely rigorous and sound. The problem is not only ensuring the estimates are relevant to your mix of hardware, but that they remain relevant over time. New designs and new manufacturing methods come online on an almost daily basis, even for what appear to be identical products. Combined with the dubious benefit at the outset, trying to maintain the integrity of the estimates is little more than a fool's errand.
With the exception of first-boot behavior, where you want to ensure there's "enough"[1] initial entropy, even accurate estimates don't provide any substantive value. Rather, mixing in a continuous stream of new entropy after you've gathered 128 or 256 bits can actually expose you to other issues, like exfiltration attacks that are more difficult to detect than they otherwise would be. (See https://blog.cr.yp.to/20140205-entropy.html)
Because of the intrinsic difficulty of maintaining accurate estimates, some people would argue that the proper way to solve the first-boot issue is to not solve it at all. The only proper solution is an on-board, robust entropy source. Anything else provides a false sense of security (eventually if not initially), disincentivizing demand for and supply of systems with an on-board source.[2] The Via C3, circa 2003, provided the first on-chip source in the commodity x86 space. Unfortunately it took Intel over a decade to add the similar RDRAND, and longer still to make it ubiquitous across their product line. Now that AMD provides RDRAND and most ARM chips have something similar, we're nearly at a point where there's no excuse for entropy collection and estimation contrivances.
IoT devices and other lower-end devices are still problematic, but those devices provide little if any hardware that entropy collection and estimation proponents can play with.
Dealing with the long tail of legacy hardware is also a PITA. The PCEngines ALIX used an AMD SoC platform (Geode LX800) with an RNG source, but the more recent APU (G-series T40 and GX-412) lacks any on-board source.[3] This issue will eventually pass, but in the meantime solutions like the NeuG provide a solution. Which isn't to say that's the only use for NeuG. There's nothing wrong with using NeuG to supplement the vendor's RNG, just keep in mind issues like exfiltration attacks for trying to use too much of a good thing.
[1] Compare Yarrow (1999) to Fortuna (2003).
[2] By on-board I mean either on the chip, on a controller (both AMD and Intel put RNG sources on some select I/O controllers), or on a pre-installed device (e.g. HSM module on high-end Unix boxes).
[3] There might actually be one on the NIC controller. I haven't been able to confirm, and in any event it's not detected by the OS and thus unsupported presuming there is one. Which alludes to another issue--the complexity created by supporting so many different mechanisms. All these device drivers and frameworks add attack surface.
But arbitrarily reading data from my camera or microphone, even for the well-intentioned purpose of generating entropy… no thank you.
As with a lot of OSHW products, they often don't make financial sense when compared to mass-market equivalents. But they have intangible advantages over them, not all of which everyone cares about.
Paranoid people might fear that those have been compromised à la Dual_EC_DRBG.
Shot noise, avalanche noise etc. are just as true. Even throwing a die would be in the same category.
Same principle would apply to all the other methods you mentioned since there's always a definite seed value, regardless of how difficult it is for you or another observer to capture it.
All the approaches you listed would result in quality pseudo-random numbers, however, a genuine random number at the source has no apparent cause or series of events leading up to it. Cesium is one such source of genuine, uninspired and uninfluenced source of randomness. Cosmic radiation is probably another although that's probably a lot harder to capture.
Here's a more detailed explanation of why radiation from radioactive decay is bona fide random.
https://www.symmetrymagazine.org/breaking/2009/03/30/real-ra...
https://engineering.mit.edu/engage/ask-an-engineer/can-a-com...
The NeuG is best described as a high quality PRNG as are most others unless they truly capture some random, cosmic phenomena, like echoes from the big bang detectable as cosmic background radiation.. Allegedly nothing tops the big bang as a random number generator.
Still no difference to shot noise or avalanche noise. You have a statistic that says how many electrons you exppect to see, but no foundational knowledge which electron will be seen at what point in time.
I find your point of view very esoteric.
There are many sources of truly random events (as in quantum), including tunneling in diodes.
Then there are chaotic systems which are good enough to be random. There, even in a deterministic universe (it doesn't appear to be) and given infinite amount of computational power (impossible), you'd still be unable predict the future (due to measurement error).
TRNG are hard to do correctly, but why are they limited to geiger counters and radioactive mtls? Is it because you can't "hack" the nuclear core (not exactly true, but sure)? Again that would be a doing the TRNG right, not a lack of randomness in physical systems.
Some years ago I used an analog TV card tuned to an empty channel that was giving seemingly white noise and captured frames to generate entropy. I only used the least significant bit of each RGB value of each pixel.
When I supplied the bits to the diehard testsuite, it complained about a number of patterns and failing tests.
The hash function will of course save you, but it's hard to tell the amount of real randomness that's coming from the sensor.
update: Oh, I may have misunderstood. If the whole images changes in random ways and is secret, then of course this is fine.
NeuG has some low quality sources of entropy, and also provides analog input for the user to supply with whatever they wish. It does not address the design of a good white noise generator, unfortunately.
you'd still have to feed that through something to get it into a PC. the STM32 is about the cheapest way of doing that. and it turns out it comes with some ADCs that are so noisy they're good sources of randomness themselves.