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Linux RNG May Be Insecure After All
Okian Warrior writes "As a followup to Linus's opinion about people skeptical of the Linux random number generator, a new paper analyzes the robustness of /dev/urandom and /dev/random . From the paper: 'From a practical side, we also give a precise assessment of the security of the two Linux PRNGs, /dev/random and /dev/urandom. In particular, we show several attacks proving that these PRNGs are not robust according to our definition, and do not accumulate entropy properly. These attacks are due to the vulnerabilities of the entropy estimator and the internal mixing function of the Linux PRNGs. These attacks against the Linux PRNG show that it does not satisfy the "robustness" notion of security, but it remains unclear if these attacks lead to actual exploitable vulnerabilities in practice.'"
Of course, you might not even be able to trust hardware RNGs. Rather than simply proving that the Linux PRNGs are not robust thanks to their run-time entropy estimator, the authors provide a new property for proving the robustness of the entropy accumulation stage of a PRNG, and offer an alternative PRNG model and proof that is both robust and more efficient than the current Linux PRNGs.
Their analysis that the linux rng is insecure under this (rather contrived) model rests on an _incorrect_ assumption that Linux stops adding to the entropy pool when the estimator concludes that the entropy pool is full.
Exactly. The maintainer of the /dev/random driver explained this and a lot more about this paper here.
Linus signs off on many changes everyday. He does expect you to read the code before trying to change it. That was the problem before - someone put up a change.org petition that made clear they had no idea how it worked.
has some thoughts on the study and the subject:
https://www.schneier.com/blog/archives/2013/10/insecurities_in.html
"Not so random" means that you can mathematically calculate how likely it is that you can predict the next number over a long time. If you can predict the next number with an accuracy of 1 in 250 while the random generator provides 1 in 1000 then the random generator isn't that random.
Many random generators picks the previous value as a seed for the next value, but that is definitely predictable. Introduce some additional factors into the equation and you lower the predictability. One real problem with random generators using previous value as a seed without adding a second factor is that they can't generate the same number twice or three times in a row (which actually is allowed under the randomness rules).
It's a completely different thing to create a true random number. For a 32 bit number you essentially should have one generator source for each bit that don't care about how the bit was set previously. It is a bit tricky to create that in a computer in a way that also allows for fast access to a large number of random numbers and prevent others from reading the same random number as you do.
For computers it's more a question of "good enough" to make prediction an unfeasible attack vector.
If builders built buildings the way programmers wrote programs, then the first woodpecker would destroy civilization.
Android. Many embedded systems. Many micro systems, such as tomsrtbt or similar (now virtually unneeded, due to the lack of floppy discs on new computers, and the prevalence of booting of CDs or USB flash drives). Many lightweight systems, such as Damn Small Linux.
Etc.
See also: Toybox.
HELP MY ACCOUNT HAS BEEN HACKED BY AN ILLIBERAL ART STUDENT SET TO DESTROY THE INTERWEBZ!