Should programming languages use cryptographic rngs like a ChaCha20 based one in their standard libraries to stop accidental use of non cryptographic rngs for cryptographic purposes? But that comes at the cost of speed
The challenge is things that don't _obviously_ need cryptographically secure generators. For example, do you need a secure generator for the seed of a hash table, or a sorting algorithm? (For those that do use a seed). Some will argue that yes, this is important. Until a few years ago, the hash tables used static hash algorithms without any randomization, but "hash flooding" changed that. I think that nowadays, still many hash table implementations don't use secure generators.
Then, there's secure and insecure hash functions. Secure hash functions like SHA-256 are (compared to non-secure functions) specially slow for short keys. There are "somewhat" secure hash function algorithms like SipHash that can be used for this purpose.
It'll never happen in Go though! They're over a decade committed to this library shape.
Secure generators are still fast and the number of programs where you'd see a difference is very small.
I ported around 20 RNGs to C# (all non-cs), and there are tons of uses for non-cryptographic RNGs, so I'm a little torn. I guess in modern development most people who need an RNG need it for crypto purposes (I would guess salts, keys and nonces mostly), but I'd hate to see all the Xoshiros, Mersenne Twisters, PCGs, and MWCs, etc. go the way of the dodo simply because they are not deemed fit for crypto purposes. Games, simulations, non-cryptographic hashes all need deterministic and high performance RNGs, and don't need all of the cryptographic guarantees.
To top it off, there is no standard definition of what makes an RNG cryptographically secure, so it's a slightly loaded question anyway. Everything I've read says an algo needs the following properties: forward secrecy (unable to guess future outputs given the current state), backward secrecy (if I know current outputs, I shouldn't be able to recover previous internal state or previous outputs), and the output must be indistinguishable from true random bits, even with a chosen-input attack. This is where I politely defer to the expert mathematicians and cryptographers, because I'm not equipped to perform such an analysis.
I can understand why things have developed this way though- people have needed random numbers far longer than they've needed cryptographically secure random numbers, so the default is the non-cryptographically secure variant. A language created tomorrow would likely follow in Go's footsteps and default to the cryptographically secure.
It's a little frustrating, because there are definitely fast RNGs that have tried to blur this line. A reasonable first approximation of the current situation is that a CSPRNG should have somewhere in its core a mixing function based on an actual cryptographic hash or permutation function; if the design has to explain what that function is and how it works (as opposed to just saying "this is ChaCha20"), it's not secure. These fast RNGs, like Xorshiro and PCG, all get there by not having cryptographically strong mixing functions at their core.
For what it's worth, I think the "GetBytes() means secure, IntN() means it's not secure" is a clever misfeature. Just make all the standard library random interfaces back onto a real CSPRNG, and let people pull in PCG or whatever if they have specialized needs for fast insecure RNGs.
That's what OpenBSD has done for the traditional C and POSIX randomness APIs.
Also, re your earlier comment, OpenBSD's arc4random API is everywhere now except linux/musl and Windows. POSIX now has getentropy, which on recent Linux kernel will be as fast as arc4random_buf. But it would be nice if musl got the arc4random API, which includes arc4random_uniform for generating 32-bit numbers in the interval [0, N), minimizing the risk of people screwing that up.
Unlikely vendors will takes OpenBSD's approach to the historic PRNG APIs, but they're almost all there for the arc4random API. Also, the former approach is less ideal than it sounds; the latest versions of PUC Lua, for example, use an included non-CSPRNG rather than merely binding the historic C APIs. Explicitly using the arc4random API means the semantics are explicit, too, and you can more easily audit code. It's conspicuously missing an API for floating point intervals, but perhaps that'll come along.
What methods, what CPU? Is that using chacha20 a couple bytes at a time? If you generate your random bytes in medium size blocks you'll probably see a much smaller difference.