Re: getentropy() vs. getrandom() vs. arc4random()

[Noah Goldstein <goldstein.w.n@gmail.com>]

On Wed, Jun 15, 2022 at 11:01 AM Adhemerval Zanella via Libc-help
<libc-help@sourceware.org> wrote:
>
>
>
> > On 15 Jun 2022, at 07:24, Fernando Gont <fernando@gont.com.ar> wrote:
> >
> > Hi,
> >
> > I'm currently trying to grasp the functional differences in the different interfaces to generate pseudorandom numbers in different platforms. And I was wondering if you could shed some light on some questions I have.
> >
> >
> > ** Brief Background: **
> >
> > We're working on a document where we warn users about the security implications of using rand() and random() to generate pseudorandom numbers (in scenarios where cryptographically secure pseudorandom numbers are needed).
> >
> > So we want to recommend better PRNG options for different operating systems. For example, in the case of OpenBSD, we recommend the use of arc4random(3), which provides a higher-level interface than the getentropy(2) system call.
> >
> > However, we're unsure about what to recommend for the Linux case.
> >
> > For the Linux case, I see that there's a lot of code using getrandom(2) -- a syscall --, which is kind of complex/too-low-level. And I see that Linux also has getentropy(3) library function, which is described in random(7) as a "more portable interface the underlying PRNG devices".
> >
> > So, for the Linux case, I feel tempted to recommend the usage of getentropy(3) over getrandom(2), but since most code employs getrandom(2), I'm not sure whether I'm missing something.
> >
> > Any thoughts?
> >
> > Aside, it seems that for OpenBSD, getentropy(2) is a "low-level" syscall, while arc4random(3) is a high-level library function. But in the case of Linux, getentropy(3) is a high-level library function instead, while getrandom(2) is the low-level syscall.  --  which means that usage of these interfaces would probably not be consistent across platforms.
> >
> > Is this actually the case?
>
> On glibc, getentropy and getrandom both end calling getrandom syscall although
> with different flags. The getentropy calls getrandom without any flag which in turn
> get entropy from /dev/urandom. The getrandom function allows us to specify
> which source you use through GRND_RANDOM flag.
>
> Also, getentropy current has a hard limit of maximum of 256 bytes and it is not
> defined a cancelation entrypoint (so pthread_cancel does not act upon it).
>
> So both functions drawn entropy direct from the kernel and with recent Linux
> random number development to unify both random and urandom the difference
> might ended up with just getentropy being a cancellation entrypoint.
>
> The rand and random functions are both userspace only where caller should set
> PRNG state and both returns predictable output based on the initial seed. On glibc
> both are implemented with either a LGC or a polynomial generated, set by the
> seed size. So the quality of the output will depend of the seed entropy and the
> limitation of the polynomial used.
>
> The arc4random is similar to getentropy and getrandom, but it tries to use kernel
> entropy to initialize a PRNG. Also, the usual implementation that uses ChaCha20
> (OpenBSD, FreeBSD) periodically feeds back kernel entropy to improve randomness.
> The arc4random also provides some more guarantees, like fork-detection.
>
> We are aiming to provide arc4random on new glibc version [1].
>
> [1] https://patchwork.sourceware.org/project/glibc/list/?series=9540

Are there any blockers to this at the moment?

Wouldn't minding have some time before 2.36 to possibly look into the
x86_64 implementations.

>
> >
> >
> > FWIW, if you're curious, the document we're working on is this one: https://www.ietf.org/archive/id/draft-irtf-pearg-numeric-ids-generation-10.txt, and the section that led me to start this thread is Section 7.1:
> >
> > ----- cut here ----
> > 7.1.  Category #1: Uniqueness (soft failure)
> >
> >   The requirement of uniqueness with a soft failure severity can be
> >   complied with a Pseudo-Random Number Generator (PRNG).
> >
> >   NOTE:
> >      Please see [RFC4086] regarding randomness requirements for
> >      security.
> >
> >   While most systems provide access to a PRNG, many of such PRNG
> >   implementations are not cryptographically secure, and therefore might
> >   be statistically biased or subject to adversarial influence.  For
> >   example, ISO C [C11] rand(3) implementations are not
> >   cryptographically secure.
> >
> >   NOTE:
> >      Section 7.1 ("Uniform Deviates") of [Press1992] discusses the
> >      underlying issues affecting ISO C [C11] rand(3) implementations.
> >
> >   On the other hand, a number of systems provide an interface to a
> >   Cryptographically Secure PRNG (CSPRNG) [RFC8937] [RFC4086], which
> >   guarantees high entropy, unpredictability, and good statistical
> >   distribution of the random values generated.  For example, GNU/
> >   Linux's CSPRNG implementation is available via the getentropy(3)
> >   interface [GETENTROPY], while OpenBSD's CSPRNG implementation is
> >   available via the arc4random(3) and arc4random_uniform(3) interfaces
> >   [ARC4RANDOM].  Where available, these CSPRNGs should be preferred
> >   over e.g.  POSIX [POSIX] random(3) or ISO C [C11] rand(3)
> >   implementations.
> >
> >   In scenarios where a CSPRNG is not readily available to select
> >   transient numeric identifiers of Category #1, a security and privacy
> >   assessment of employing a regular PRNG should be performed,
> >   supporting the implementation decision.
> >
> >   NOTE:
> >      [Aumasson2018], [Press1992], and [Knuth1983], discuss theoretical
> >      and practical aspects of pseudorandom numbers generation, and
> >      provide guidance on how to evaluate PRNGs.
> >
> >   We note that since the premise is that collisions of transient
> >   numeric identifiers of this category only leads to soft failures, in
> >   many cases, the algorithm might not need to check the suitability of
> >   a selected identifier (i.e., the suitable_id() function, described
> >   below, could always return "true").
> > ---- cut here ----
> >
> > Thanks a lot in advance!
> >
> > Regards,
> > --
> > Fernando Gont
> > e-mail: fernando@gont.com.ar
> > PGP Fingerprint: 7809 84F5 322E 45C7 F1C9 3945 96EE A9EF D076 FFF1
>