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In an effort to provide a flexible implementation for a random number generator that also delivers entropy during early boot time, allows replacement of the deterministic random number generation mechanism, implement the various components in separate code for easier maintenance, and provide compliance to SP800-90[A|B|C], introduce the Linux Random Number Generator (LRNG) framework. The general design is as follows. Additional implementation details are given in [1]. The LRNG consists of the following components: 1. The LRNG implements a DRNG. The DRNG always generates the requested amount of output. When using the SP800-90A terminology it operates without prediction resistance. The secondary DRNG maintains a counter of how many bytes were generated since last re-seed and a timer of the elapsed time since last re-seed. If either the counter or the timer reaches a threshold, the secondary DRNG is seeded from the entropy pool. In case the Linux kernel detects a NUMA system, one secondary DRNG instance per NUMA node is maintained. 2. The DRNG is seeded by concatenating the data from the following sources: (a) the output of the entropy pool, (b) the Jitter RNG if available and enabled, and (c) the CPU-based noise source such as Intel RDRAND if available and enabled. The entropy estimate of the data of all noise sources are added to form the entropy estimate of the data used to seed the DRNG with. The LRNG ensures, however, that the DRNG after seeding is at maximum the security strength of the DRNG. The LRNG is designed such that none of these noise sources can dominate the other noise sources to provide seed data to the DRNG during due to the following: (a) During boot time, the amount of received interrupts are the trigger points to (re)seed the DRNG. (b) At runtime, the available entropy from the slow noise source is concatenated with a pre-defined amount of data from the fast noise sources. In addition, each DRNG reseed operation triggers external noise source providers to deliver one block of data. 3. The entropy pool accumulates entropy obtained from certain events, which will henceforth be collectively called "slow noise sources". The entropy pool collects noise data from slow noise sources. Any data received by the LRNG from the slow noise sources is inserted into a per-CPU entropy pool using a hash operation that can be changed during runtime. Per default, SHA-256 is used. (a) When an interrupt occurs, the high-resolution time stamp is mixed into the per-CPU entropy pool. This time stamp is credited with heuristically implied entropy. (b) HID event data like the key stroke or the mouse coordinates are mixed into the per-CPU entropy pool. This data is not credited with entropy by the LRNG. (c) Device drivers may provide data that is mixed into an auxiliary pool using the same hash that is used to process the per-CPU entropy pool. This data is not credited with entropy by the LRNG. Any data provided from user space by either writing to /dev/random, /dev/urandom or the IOCTL of RNDADDENTROPY on both device files are always injected into the auxiliary pool. In addition, when a hardware random number generator covered by the Linux kernel HW generator framework wants to deliver random numbers, it is injected into the auxiliary pool as well. HW generator noise source is handled separately from the other noise source due to the fact that the HW generator framework may decide by itself when to deliver data whereas the other noise sources always requested for data driven by the LRNG operation. Similarly any user space provided data is inserted into the entropy pool. When seed data for the DRNG is to be generated, all per-CPU entropy pools and the auxiliary pool are hashed. The message digest forms the new auxiliary pool state. At the same time, this data is used for seeding the DRNG. To speed up the interrupt handling code of the LRNG, the time stamp collected for an interrupt event is truncated to the 8 least significant bits. 64 truncated time stamps are concatenated and then jointly inserted into the per-CPU entropy pool. During boot time, until the fully seeded stage is reached, each time stamp with its 32 least significant bits is are concatenated. When 16 such events are received, they are injected into the per-CPU entropy pool. The LRNG allows the DRNG mechanism to be changed at runtime. Per default, a ChaCha20-based DRNG is used. The ChaCha20-DRNG implemented for the LRNG is also provided as a stand-alone user space deterministic random number generator. The LRNG also offers an SP800-90A DRBG based on the Linux kernel crypto API DRBG implementation. The processing of entropic data from the noise source before injecting them into the DRNG is performed with the following mathematical operations: 1. Truncation: The received time stamps are truncated to 8 least significant bits (or 32 least significant bits during boot time) 2. Concatenation: The received and truncated time stamps as well as auxiliary 32 bit words are concatenated to fill the per-CPU data array that is capable of holding 64 8-bit words. 3. Hashing: A set of concatenated time stamp data received from the interrupts are hashed together with the current existing per-CPU entropy pool state. The resulting message digest is the new per-CPU entropy pool state. 4. Hashing: When new data is added to the auxiliary pool, the data is hashed together with the auxiliary pool to form a new auxiliary pool state. 5. Hashing: A message digest of all per-CPU entropy pools and the auxiliary pool is calculated which forms the new auxiliary pool state. At the same time, this message digest is used to fill the slow noise source output buffer discussed in the following. 6. Truncation: The most-significant bits (MSB) defined by the requested number of bits (commonly equal to the security strength of the DRBG) or the entropy available transported with the buffer (which is the minimum of the message digest size and the available entropy in all entropy pools and the auxiliary pool), whatever is smaller, are obtained from the slow noise source output buffer. 7. Concatenation: The temporary seed buffer used to seed the DRNG is a concatenation of the slow noise source buffer, the Jitter RNG output, the CPU noise source output, and the current time. The DRNG always tries to seed itself with 256 bits of entropy, except during boot. In any case, if the noise sources cannot deliver that amount, the available entropy is used and the DRNG keeps track on how much entropy it was seeded with. The entropy implied by the LRNG available in the entropy pool may be too conservative. To ensure that during boot time all available entropy from the entropy pool is transferred to the DRNG, the hash_df function always generates 256 data bits during boot to seed the DRNG. During boot, the DRNG is seeded as follows: 1. The DRNG is reseeded from the entropy pool and potentially the fast noise sources if the entropy pool has collected at least 32 bits of entropy from the interrupt noise source. The goal of this step is to ensure that the DRNG receives some initial entropy as early as possible. In addition it receives the entropy available from the fast noise sources. 2. The DRNG is reseeded from the entropy pool and potentially the fast noise sources if all noise sources collectively can provide at least 128 bits of entropy. 3. The DRNG is reseeded from the entropy pool and potentially the fast noise sources if all noise sources collectivel can provide at least 256 bits. At the time of the reseeding steps, the DRNG requests as much entropy as is available in order to skip certain steps and reach the seeding level of 256 bits. This may imply that one or more of the aforementioned steps are skipped. In all listed steps, the DRNG is (re)seeded with a number of random bytes from the entropy pool that is at most the amount of entropy present in the entropy pool. This means that when the entropy pool contains 128 or 256 bits of entropy, the DRNG is seeded with that amount of entropy as well. Before the DRNG is seeded with 256 bits of entropy in step 3, requests of random data from /dev/random and the getrandom system call are not processed. The hash operation providing random data from the entropy pools will always require that all entropy sources collectively can deliver at least 128 entropy bits. The DRNG operates as deterministic random number generator with the following properties: * The maximum number of random bytes that can be generated with one DRNG generate operation is limited to 4096 bytes. When longer random numbers are requested, multiple DRNG generate operations are performed. The ChaCha20 DRNG as well as the SP800-90A DRBGs implement an update of their state after completing a generate request for backtracking resistance. * The secondary DRNG is reseeded with whatever entropy is available – in the worst case where no additional entropy can be provided by the noise sources, the DRNG is not re-seeded and continues its operation to try to reseed again after again the expiry of one of these thresholds: - If the last reseeding of the secondary DRNG is more than 600 seconds ago, or - 2^20 DRNG generate operations are performed, whatever comes first, or - the secondary DRNG is forced to reseed before the next generation of random numbers if data has been injected into the LRNG by writing data into /dev/random or /dev/urandom. The chosen values prevent high-volume requests from user space to cause frequent reseeding operations which drag down the performance of the DRNG. With the automatic reseeding after 600 seconds, the LRNG is triggered to reseed itself before the first request after a suspend that put the hardware to sleep for longer than 600 seconds. To support smaller devices including IoT environments, this patch allows reducing the runtime memory footprint of the LRNG at compile time by selecting smaller collection data sizes. When selecting the compilation of a kernel for a small environment, prevent the allocation of a buffer up to 4096 bytes to serve user space requests. In this case, the stack variable of 64 bytes is used to serve all user space requests. The LRNG has the following properties: * internal noise source: interrupts timing with fast boot time seeding * high performance of interrupt handling code: The LRNG impact on the interrupt handling has been reduced to a minimum. On one example system, the LRNG interrupt handling code in its fastest configuration executes within an average 55 cycles whereas the existing /dev/random on the same device takes about 97 cycles when measuring the execution time of add_interrupt_randomness(). * use of almost never contended lock for hashing operation to collect raw entropy supporting concurrency-free use of massive parallel systems - worst case rate of contention is the number of DRNG reseeds, usually: number of NUMA nodes contentions per 5 minutes. * use of standalone ChaCha20 based RNG with the option to use a different DRNG selectable at compile time * instantiate one DRNG per NUMA node * support for runtime switchable output DRNGs * use of runtime-switchable hash for conditioning implementation following widely accepted approach * compile-time selectable collection size * support of small systems by allowing the reduction of the runtime memory needs Further details including the rationale for the design choices and properties of the LRNG together with testing is provided at [1]. In addition, the documentation explains the conducted regression tests to verify that the LRNG is API and ABI compatible with the existing /dev/random implementation. [1] https://www.chronox.de/lrng.html CC: Torsten Duwe <duwe@lst.de> CC: "Eric W. Biederman" <ebiederm@xmission.com> CC: "Alexander E. Patrakov" <patrakov@gmail.com> CC: "Ahmed S. Darwish" <darwish.07@gmail.com> CC: "Theodore Y. Ts'o" <tytso@mit.edu> CC: Willy Tarreau <w@1wt.eu> CC: Matthew Garrett <mjg59@srcf.ucam.org> CC: Vito Caputo <vcaputo@pengaru.com> CC: Andreas Dilger <adilger.kernel@dilger.ca> CC: Jan Kara <jack@suse.cz> CC: Ray Strode <rstrode@redhat.com> CC: William Jon McCann <mccann@jhu.edu> CC: zhangjs <zachary@baishancloud.com> CC: Andy Lutomirski <luto@kernel.org> CC: Florian Weimer <fweimer@redhat.com> CC: Lennart Poettering <mzxreary@0pointer.de> CC: Nicolai Stange <nstange@suse.de> CC: Alexander Lobakin <alobakin@mailbox.org> Mathematical aspects Reviewed-by: "Peter, Matthias" <matthias.peter@bsi.bund.de> Reviewed-by: Marcelo Henrique Cerri <marcelo.cerri@canonical.com> Reviewed-by: Roman Drahtmueller <draht@schaltsekun.de> Tested-by: Marcelo Henrique Cerri <marcelo.cerri@canonical.com> Tested-by: Neil Horman <nhorman@redhat.com> Signed-off-by: Stephan Mueller <smueller@chronox.de>
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