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GoDoc Go Report Card

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frand is a fast-key-erasure CSPRNG in userspace. Its output is sourced from the keystream of a ChaCha cipher, much like the CSPRNG in the Linux and BSD kernels. The initial cipher key is derived from the kernel CSPRNG, after which no new entropy is ever mixed in.

The primary advantage of frand over crypto/rand is speed: when generating large amounts of random data, frand is 20x faster than crypto/rand, and over 100x faster when generating data in parallel. frand is also far more convenient to use than crypto/rand, as its methods cannot return errors, and helper methods such as Intn and Perm are provided. In short, it is as fast and convenient as math/rand, but far more secure.


frand closely follows the FKE-CSPRNG design linked above. The generator maintains a buffer consisting of a ChaCha key followed by random data. When a caller requests data, the generator fills the request from its buffer, and immediately overwrites that portion of the buffer with zeros. If the buffer is exhausted, the generator "refills" the buffer by xoring it with the ChaCha key's keystream; thus, the key is only used once, and immediately overwrites itself. This provides forward secrecy: even if an attacker learns the secret key, they cannot learn what data was previously output from the generator.

One optimization is implemented. If the caller requests a large amount of data (larger than the generator's buffer), the generator does not repeatedly fill and drain its buffer. Instead, it requests 32 bytes from itself, and uses this as a ChaCha key to directly generate the amount of data requested. The key is then overwritten with zeros.

The default generator uses ChaCha12. ChaCha8 is significantly weaker without being much faster; ChaCha20 is significantly slower without being much stronger.

At init time, a "master" generator is created using an initial key sourced from crypto/rand. New generators source their initial keys from this master generator. Following djb's recommendation, the generator never mixes in new entropy.

Calls to global functions like Read and Intn are serviced by a sync.Pool of generators derived from the master generator. This allows frand to scale near-linearly across CPU cores, which is what makes it so much faster than crypto/rand and math/rand in parallel benchmarks.


Some may object to substituting a userspace CSPRNG for the kernel's CSPRNG. Certain userspace CSPRNGs (e.g. OpenSSL) have contained bugs, causing serious vulnerabilities. frand has some advantages over these CSPRNGs: it never mixes in new entropy, it doesn't maintain an "entropy estimate," and its implementation is extremely simple. Still, if you only need to generate a handful of secret keys, you should probably stick with crypto/rand.

Even if you aren't comfortable using frand for critical secrets, you should still use it everywhere you would normally use an insecure PRNG like math/rand. Go programmers frequently use math/rand because it is faster and more convenient than crypto/rand, and only use crypto/rand when "true" randomness is required. This is an unfortunate state of affairs, because misuse of math/rand can lead to bugs or vulnerabilities. For example, using math/rand to generate "random" test cases will produce identical coverage from run-to-run if the generator is left unseeded. More seriously, using math/rand to salt a hash table may make your program vulnerable to denial of service attacks. Substituting frand for math/rand would avoid both of these outcomes.


Benchmark frand crypto/rand math/rand (insecure) fastrand
Read (32b) 964 MB/s 59 MB/s 634.21 MB/s 215 MB/s
Read (32b, concurrent) 3566 MB/s 70 MB/s 198.97 MB/s 615 MB/s
Read (512kb) 5094 MB/s 239 MB/s 965.85 MB/s 452 MB/s
Read (512kb, concurrent) 19665 MB/s 191 MB/s 958.01 MB/s 1599 MB/s
Intn (n =~ 4e18) 45 ns/op 725 ns/op 20 ns/op 210 ns/op
BigIntn (n = 2^630) 223 ns/op 1013 ns/op 295 ns/op 468 ns/op
Perm (n = 32) 954 ns/op 17197 ns/op 789 ns/op 5021 ns/op

Benchmark details:

"Concurrent" means the Read function was called in parallel from 64 goroutines.

Intn was benchmarked with n = 2^62 + 1, which maximizes the number of expected retries required to remove bias. The number of expected retries is 1.333.