Messages in this thread | | | Date | Sat, 17 Aug 2002 21:15:22 -0500 | From | Oliver Xymoron <> | Subject | [PATCH] (0/4) Entropy accounting fixes |
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I've done an analysis of entropy collection and accounting in current Linux kernels and founds some major weaknesses and bugs. As entropy accounting is only one part of the security of the random number device, it's unlikely that these flaws are compromisable, nonetheless it makes sense to fix them.
- Broken analysis of entropy distribution - Spoofable delta model - Interrupt timing independence - Ignoring time scale of entropy sources - Confusion of unpredictable and merely complex sources and trusting the latter - Broken pool transfers - Entropy pool can be overrun with untrusted data
Net effect: a typical box will claim to generate 2-5 _orders of magnitude_ more entropy than it actually does.
Note that entropy accounting is mostly useful for things like the generation of large public key pairs where the number of bits of entropy in the key is comparable to the size of the PRNG's internal state. For most purposes, /dev/urandom is still more than strong enough to make attacking a cipher directly more productive than attacking the PRNG.
The following patches against 2.5.31 have been tested on x86, but should compile elsewhere just fine.
I've tried to cover some of the issues in detail below:
Broken analysis of entropy distribution ---------------------------------------
(I know the topic of entropy is rather poorly understood, so here's a couple useful pieces of background for kernel folks:
Cryptanalytic Attacks on Pseudorandom Number Generators Kelsey, Schneier, Wagner, Hall www.counterpane.com/pseudorandom_number.pdf
Cryptographic Randomness from Air Turbulence in Disk Drives D. Davis, R. Ihaka, P.R. Fenstermacher http://world.std.com/~dtd/random/forward.ps)
Mathematically defining entropy
For a probability distribution P of samples K, the entropy is:
E = sum (-P(K) * log2 P(K))
For a uniform distribution of n bits of data, the entropy is n. Anything other than a uniform distribution has less than n bits of entropy.
Non-Uniform Distribution Of Timing
Unfortunately, our sample source is far from uniform. For starters, each interrupt has a finite time associated with it - the interrupt latency. Back to back interrupts will result in samples that are periodically spaced by a fixed interval.
A priori, we might expect a typical interrupt to be a Poisson process, resulting in a gamma-like distribution. It would also have zero probability up to some minimum latency, have a peak at minimum latency representing the likelihood of back-to-back interrupts, a smooth hump around the average interrupt rate, and an infinite tail.
Not surprisingly, this distribution has less entropy in it than a uniform distribution would. Linux takes the approach of assuming the distribution is "scale invariant" (which is true for exponential distributions and approximately true for the tails of gamma distributions) and that the amount of entropy in a sample is in relation to the number of bits in a given interrupt delta.
Assuming the interrupt actually has a nice gamma-like distribution (which is unlikely in practice), then this is indeed true. The trouble is that Linux assumes that if a delta is 13 bits, it contains 12 bits of actual entropy. A moment of thought will reveal that binary numbers of the form 1xxxx can contain at most 4 bits of entropy - it's a tautology that all binary numbers start with 1 when you take off the leading zeros. This is actually a degenerate case of Benford's Law (http://mathworld.wolfram.com/BenfordsLaw.html), which governs the distribution of leading digits in scale invariant distributions.
What we're concerned with is the entropy contained in digits following the leading 1, which we can derive with a simple extension of Benford's Law (and some Python):
def entropy(l): s=0 for pk in l: if pk: s=s+(-pk*log2(pk)) return s
def benford(digit, place=0, base=10): if not place: s=log(1+1.0/digit) else: s=0 for k in range(base**(place-1), (base**place)): s=s+log(1+1.0/(k*base+digit)) print k,s
return s/log(base)
for b in range(3,16): l=[] for k in range(1,(2**(b-1))-1): l.append(benford(k,0,2**(b-1))) print "%2d %6f" % (b, entropy(l))
Which gives us: 3 1.018740 4 2.314716 5 3.354736 6 4.238990 7 5.032280 8 5.769212 9 6.468756 10 7.141877 11 7.795288 12 8.433345 13 9.059028 14 9.674477 15 10.281286 As it turns out, our 13-bit number has at most 9 bits of entropy, and as we'll see in a bit, probably significantly less.
All that said, this is easily dealt with by lookup table.
Interrupt Timing Independence -----------------------------
Linux entropy estimate also wrongly assumes independence of different interrupt sources. While SMP complicates the matter, this is generally not the case. Low-priority interrupts must wait on high priority ones and back to back interrupts on shared lines will serialize themselves ABABABAB. Further system-wide CLI, cache flushes and the like will skew -all- the timings and cause them to bunch up in predictable fashion.
Furthermore, all this is observable from userspace in the same way that worst-case latency is measured.
To protect against back to back measurements and userspace observation, we insist that at least one context switch has occurred since we last sampled before we trust a sample. Questionable Sources and Time Scales ------------------------------------
Due to the vagarities of computer architecture, things like keyboard and mouse interrupts occur on their respective scanning or serial clock edges, and are clocked relatively slowly. Worse, devices like USB keyboards, mice, and disks tend to share interrupts and probably line up on USB clock boundaries. Even PCI interrupts have a granularity on the order of 33MHz (or worse, depending on the particular adapter), which when timed by a fast processor's 2GHz clock, make the low six bits of timing measurement predictable.
And as far as I can find, no one's tried to make a good model or estimate of actual keyboard or mouse entropy. Randomness caused by disk drive platter turbulence has actually been measured and is on the order of 100bits/minute and is well correlated on timescales of seconds - we're likely way overestimating it.
We can deal with this by having each trusted source declare its clock resolution and removing extra timing resolution bits when we make samples.
Trusting Predictable or Measurable Sources ------------------------------------------
What entropy can be measured from disk timings are very often leaked by immediately relaying data to web, shell, or X clients. Further, patterns of drive head movement can be remotely controlled by clients talking to file and web servers. Thus, while disk timing might be an attractive source of entropy, it can't be used in a typical server environment without great caution.
Complexity of analyzing timing sources should not be confused with unpredictability. Disk caching has no entropy, disk head movement has entropy only to the extent that it creates turbulence. Network traffic is potentially completely observable.
(Incidentally, tricks like Matt Blaze's truerand clock drift technique probably don't work on most PCs these days as the "realtime" clock source is often derived directly from the bus/PCI/memory/CPU clock.)
If we're careful, we can still use these timings to seed our RNG, as long as we don't account them as entropy.
Batching --------
Samples to be mixed are batched into a 256 element ring buffer. Because this ring isn't allowed to wrap, it's dangerous to store untrusted samples as they might flood out trusted ones.
We can allow untrusted data to be safely added to the pool by XORing new samples in rather than copying and allowing the pool to wrap around. As non-random data won't be correlated with random data, this mixing won't destroy any entropy.
Broken Pool Transfers ---------------------
Worst of all, the accounting of entropy transfers between the primary and secondary pools has been broken for quite some time and produces thousands of bits of entropy out of thin air.
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