5 ms·

Pseudo-random! A new programmer reading this article would come away with the impression that, if they need random numbers, they should use xorshift or PCG, wh

Pseudo-random! A new programmer reading this article would come away with the impression that, if they need random numbers, they should use xorshift or PCG, when in reality they should be calling getentropy(), or, if a syscall is too expensive, using a CSPRNG (e.g. ChaCha or BLAKE3) seeded with getentropy(). We now have RNGs that are both secure and really, really fast -- multiple GB/s fast -- so there are very few circumstances where a PRNG is truly necessary.

SideQuark 4y ago
If people call getentropy() when it;s not needed, it lowers entropy for places that do need it. There's not an infinite amount of crypto secure randomness available for all processes to go nuts sucking it up. This is one reason not to use getentropy as a PRNG - it will cause problems with other entropy needs. So advising people to just suck it up when they feel like it without truly needing it is a bad idea. If one needs fast PRNGs, say for simulation, monte carlo stuff, etc. then CSPRNGs are a terrible idea. They're literally orders of magnitude slower than fast PRNGs. Almost nothing needs CSPRNGs (only things needing crypto level security, which is a tiny amount of the uses for PRNGs). In short, use the right tool for the job.
- nemo1618 4y ago
  > There's not an infinite amount of crypto secure randomness available for all processes to go nuts sucking it up In fact, there is! The idea that your kernel has some finite amount of entropy that can be "used up" is a persistent myth. See https://www.2uo.de/myths-about-urandom/ https://www.2uo.de/myths-about-urandom/ Okay, it's technically not infinite, but 128 bits of "true entropy" is enough to seed a CSPRNG that will generate as many random numbers as you will ever need. > Almost nothing needs CSPRNGs This is how we end up with, e.g., hashmap implementations that use non-cryptographic hash functions, and then an attacker DoS's your server with 10,000 keys that all end up in the same bucket. Or "random" automated tests that always use the same PRNG with the same seed, meaning certain code paths will never be taken. We used to live in a world where developers frequently had to choose between safety and performance. Except for a few niche applications, that is no longer the case: we can have both. I certainly agree that there are situations where maximum performance is required, and security is pointless -- but 99% of the time, developers should not even be asking that question. They should just be using the default RNG provided by their language, which should be a CSPRNG, not a PRNG.
  - SideQuark 4y ago
    >The idea that your kernel has some finite amount of entropy that can be "used up" is a persistent myth Thomas' Digital Garden blog is not really the place to find good advice on this. Crypto researchers are the place to look. The quality of how Linux handles this is quite open to debate, and researchers routinely question the choies made. Here's [1] one of many. Use Google Scholar, enter urandom, and limit the search to recently to see more. The way urandom does stretching does not provide entropy (which follows from the second law of thermo). And yes, there is a finite amount. Developers have decided to conflate actual entropy with "hard to compute," which is simply not true. Yes, you can get 128 bits of true entropy, and then use a stream cipher to reuse it over and over, but it's not adding or making more entropy. It's simply that the stream cipher is not (yet?) broken, at which point you'll realize there is not enough entropy. By the argument of entropy stretching, you could claim you only need 10, or 2, or 1 bit of entropy, and then use a stream cipher, and have unlimited entropy. Since that would be broken quickly, showing the lie, they choose 128 or 256 or so, and hope the cipher method doesn't break. And yes, getting more and more from that stream weakens the system, and eventually, like all crpyto, the method will break. >This is how we end up with, e.g., hashmap implementations that use non-cryptographic hash functions, and then an attacker DoS's your server with 10,000 keys Conversely, you use an algorithm that takes 100x of the time to hash, then they simply DoS's your server by sending it any large batch of things to hash. For many hashes the PRNG is a significant amount of time used. This is why there is no one size fits all method. I have worked in crypto (and high speed computing and numerics) for decades, and have patents in crypto stuff, and have even written articles about PRNGs (and given talks on how to break them via SAT solvers and Z3 stuff), so I am well aware of the uses of all these pieces. If some area needs crypto, assuming an amateur will simply throw in a proper function call and now magically have security is terrible way to design or implement systems. If an area needs crypto, have a senior person decide what to do, and walk the person that is left guessing through how it works. Your advice leads to people thinking (incorrectly) that since they called getentropy to seed, now they are secure, when there is a zillion other attacks they are still open to (timing, forgetting how to use block ciphers, choosing the wrong hash map type in the case of hash maps, not handling salt correctly, not using memory buffers properly, not ensuring contents don't leak, ensuring things running in the cloud don't leak, and on and on..., forgetting any of a ton of things needed to make the thing secure). If you're worried about DDOS, paying 100x time for a PRNG call can lead to similar system failures - in gaming, rendering, simulation, science stuff. So the same advice: use the one that is most suited. I always recommend default to faster since someone not knowing about crypto should ever be implementing things that accidentally need crypto at such a tiny scale. And the slowdowns hit all software. >Except for a few niche applications The majority of running code in the world is not facing attackers - it's stuff like embedded systems, medical devices, tools and toys, programs on my PC (only a tiny few face the world), data processing systems (finance, billing, logging, tracking, inventory...), and so on. The largest use of PRNG calls are simulation by far, which most certainly do not want to pay the 100x performance hit. The niche applications are those needing a CSPRNG, which is truly the smaller fraction of pieces of code written. >Or "random" automated tests that always use the same PRNG with the same seed, meaning certain code paths will never be taken Repeatable tests are by far easiest to fix. I've seen tons of people do exactly what you say, and get an error they never see again. The correct answer here is not to rely on randomness executing a path in your code for testing. Make sure that the code is tested properly, not randomly. Relying on the luck of random number generators is simply bad advice. Use them to get more values, or to stress test for load testing, but expecting a lucky number pick to happen on test 1 of a trillion runs is not very solid advice compared to simply running the test, repeatably, with enough runs to provide the level of stress you desire. At least then you can redo the test and find the bug and fix it in reasonable time. >that is no longer the case: we can have both No, we do not. I regularly work on crypto code and often get called in to go over stuff with others. I also write lots of high performance code (mostly scientific, simulation, and the occasional rendering needs) that needs speed. There is no PRNG that meets both needs by a long shot. > They should just be using the default RNG provided by their language, Agreed. > which should be a CSPRNG, not a PRNG And that is not true for any language I am aware of for the reasons I mentioned. Python: Mersenne Twister C#: Xoshiro JavaScript: xorshift128+ Go: Additive Lagged Fibonacci C/C++: large range, from really bad to mostly bad Java: LCG And so on...... [1] "An Empirical Study on the Quality of Entropy Sources in Linux Random Number Generator", https://ieeexplore.ieee.org/abstract/document/9839285 https://ieeexplore.ieee.org/abstract/document/9839285
whyever 4y ago
The linked text is from a Rust library for generating random numbers where predictability is acceptable, i.e. it does not concern itself with cryptographic security. The more popular library rand usese ChaCha and getentropy as you described.
denton-scratch 4y ago
> when in reality they should be calling getentropy() A new programmer shouldn't be meddling in cryptography, so they probably don't need either cryptographically-secure pseudo-random numbers nor true random numbers. True random numbers are tricky.
- nemo1618 4y ago
  My whole point is that cryptographically-secure should be the default, as there are many scenarios where a PRNG leads to a security vulnerability where a CSPRNG would not. It is precisely new programmers who should be using CSPRNGs for everything, because they are the least well-equipped to know when strong entropy is necessary! We should (almost) never be asking "Do you really need a CSPRNG?" but rather "Do you really need a PRNG?"
  - denton-scratch 4y ago
    > because they are the least well-equipped to know when strong entropy is necessary! Yuh. I'm not sure what "strong entropy" means, in this context; entropy's usually reported as some number of bits of entropy. So perhaps "a lot of entropy" is clearer. At any rate, by default a (CS)PRNG doesn't have any entropy that isn't present in its seed. According to some, at least, that entropy is diminished every time you read from the RNG, so it depletes to nothing after a finite number of reads. I've finally come to the conclusion that entropy, whatever that means, is orthogonal to RNGs. Instead, RNGs should be classified by their unpredictability. A CSPRNG is one with high unpredictability. And I've given up on trying to build a DIY HWRNG. It was a misbegotten project.
thanatropism 4y ago
Many people actually want low-discrepancy sequences anyway. https://en.wikipedia.org/wiki/Low-discrepancy_sequence https://en.wikipedia.org/wiki/Low-discrepancy_sequence
- beyondCritics 4y ago
  Should become more attention.

SideQuark 4y ago

If people call getentropy() when it;s not needed, it lowers entropy for places that do need it. There's not an infinite amount of crypto secure randomness available for all processes to go nuts sucking it up. This is one reason not to use getentropy as a PRNG - it will cause problems with other entropy needs. So advising people to just suck it up when they feel like it without truly needing it is a bad idea. If one needs fast PRNGs, say for simulation, monte carlo stuff, etc. then CSPRNGs are a terrible idea. They're literally orders of magnitude slower than fast PRNGs. Almost nothing needs CSPRNGs (only things needing crypto level security, which is a tiny amount of the uses for PRNGs). In short, use the right tool for the job.

nemo1618 4y ago
> There's not an infinite amount of crypto secure randomness available for all processes to go nuts sucking it up In fact, there is! The idea that your kernel has some finite amount of entropy that can be "used up" is a persistent myth. See https://www.2uo.de/myths-about-urandom/ https://www.2uo.de/myths-about-urandom/ Okay, it's technically not infinite, but 128 bits of "true entropy" is enough to seed a CSPRNG that will generate as many random numbers as you will ever need. > Almost nothing needs CSPRNGs This is how we end up with, e.g., hashmap implementations that use non-cryptographic hash functions, and then an attacker DoS's your server with 10,000 keys that all end up in the same bucket. Or "random" automated tests that always use the same PRNG with the same seed, meaning certain code paths will never be taken. We used to live in a world where developers frequently had to choose between safety and performance. Except for a few niche applications, that is no longer the case: we can have both. I certainly agree that there are situations where maximum performance is required, and security is pointless -- but 99% of the time, developers should not even be asking that question. They should just be using the default RNG provided by their language, which should be a CSPRNG, not a PRNG.
- SideQuark 4y ago
  >The idea that your kernel has some finite amount of entropy that can be "used up" is a persistent myth Thomas' Digital Garden blog is not really the place to find good advice on this. Crypto researchers are the place to look. The quality of how Linux handles this is quite open to debate, and researchers routinely question the choies made. Here's [1] one of many. Use Google Scholar, enter urandom, and limit the search to recently to see more. The way urandom does stretching does not provide entropy (which follows from the second law of thermo). And yes, there is a finite amount. Developers have decided to conflate actual entropy with "hard to compute," which is simply not true. Yes, you can get 128 bits of true entropy, and then use a stream cipher to reuse it over and over, but it's not adding or making more entropy. It's simply that the stream cipher is not (yet?) broken, at which point you'll realize there is not enough entropy. By the argument of entropy stretching, you could claim you only need 10, or 2, or 1 bit of entropy, and then use a stream cipher, and have unlimited entropy. Since that would be broken quickly, showing the lie, they choose 128 or 256 or so, and hope the cipher method doesn't break. And yes, getting more and more from that stream weakens the system, and eventually, like all crpyto, the method will break. >This is how we end up with, e.g., hashmap implementations that use non-cryptographic hash functions, and then an attacker DoS's your server with 10,000 keys Conversely, you use an algorithm that takes 100x of the time to hash, then they simply DoS's your server by sending it any large batch of things to hash. For many hashes the PRNG is a significant amount of time used. This is why there is no one size fits all method. I have worked in crypto (and high speed computing and numerics) for decades, and have patents in crypto stuff, and have even written articles about PRNGs (and given talks on how to break them via SAT solvers and Z3 stuff), so I am well aware of the uses of all these pieces. If some area needs crypto, assuming an amateur will simply throw in a proper function call and now magically have security is terrible way to design or implement systems. If an area needs crypto, have a senior person decide what to do, and walk the person that is left guessing through how it works. Your advice leads to people thinking (incorrectly) that since they called getentropy to seed, now they are secure, when there is a zillion other attacks they are still open to (timing, forgetting how to use block ciphers, choosing the wrong hash map type in the case of hash maps, not handling salt correctly, not using memory buffers properly, not ensuring contents don't leak, ensuring things running in the cloud don't leak, and on and on..., forgetting any of a ton of things needed to make the thing secure). If you're worried about DDOS, paying 100x time for a PRNG call can lead to similar system failures - in gaming, rendering, simulation, science stuff. So the same advice: use the one that is most suited. I always recommend default to faster since someone not knowing about crypto should ever be implementing things that accidentally need crypto at such a tiny scale. And the slowdowns hit all software. >Except for a few niche applications The majority of running code in the world is not facing attackers - it's stuff like embedded systems, medical devices, tools and toys, programs on my PC (only a tiny few face the world), data processing systems (finance, billing, logging, tracking, inventory...), and so on. The largest use of PRNG calls are simulation by far, which most certainly do not want to pay the 100x performance hit. The niche applications are those needing a CSPRNG, which is truly the smaller fraction of pieces of code written. >Or "random" automated tests that always use the same PRNG with the same seed, meaning certain code paths will never be taken Repeatable tests are by far easiest to fix. I've seen tons of people do exactly what you say, and get an error they never see again. The correct answer here is not to rely on randomness executing a path in your code for testing. Make sure that the code is tested properly, not randomly. Relying on the luck of random number generators is simply bad advice. Use them to get more values, or to stress test for load testing, but expecting a lucky number pick to happen on test 1 of a trillion runs is not very solid advice compared to simply running the test, repeatably, with enough runs to provide the level of stress you desire. At least then you can redo the test and find the bug and fix it in reasonable time. >that is no longer the case: we can have both No, we do not. I regularly work on crypto code and often get called in to go over stuff with others. I also write lots of high performance code (mostly scientific, simulation, and the occasional rendering needs) that needs speed. There is no PRNG that meets both needs by a long shot. > They should just be using the default RNG provided by their language, Agreed. > which should be a CSPRNG, not a PRNG And that is not true for any language I am aware of for the reasons I mentioned. Python: Mersenne Twister C#: Xoshiro JavaScript: xorshift128+ Go: Additive Lagged Fibonacci C/C++: large range, from really bad to mostly bad Java: LCG And so on...... [1] "An Empirical Study on the Quality of Entropy Sources in Linux Random Number Generator", https://ieeexplore.ieee.org/abstract/document/9839285 https://ieeexplore.ieee.org/abstract/document/9839285

whyever 4y ago

The linked text is from a Rust library for generating random numbers where predictability is acceptable, i.e. it does not concern itself with cryptographic security. The more popular library rand usese ChaCha and getentropy as you described.

denton-scratch 4y ago

> when in reality they should be calling getentropy() A new programmer shouldn't be meddling in cryptography, so they probably don't need either cryptographically-secure pseudo-random numbers nor true random numbers. True random numbers are tricky.

nemo1618 4y ago
My whole point is that cryptographically-secure should be the default, as there are many scenarios where a PRNG leads to a security vulnerability where a CSPRNG would not. It is precisely new programmers who should be using CSPRNGs for everything, because they are the least well-equipped to know when strong entropy is necessary! We should (almost) never be asking "Do you really need a CSPRNG?" but rather "Do you really need a PRNG?"
- denton-scratch 4y ago
  > because they are the least well-equipped to know when strong entropy is necessary! Yuh. I'm not sure what "strong entropy" means, in this context; entropy's usually reported as some number of bits of entropy. So perhaps "a lot of entropy" is clearer. At any rate, by default a (CS)PRNG doesn't have any entropy that isn't present in its seed. According to some, at least, that entropy is diminished every time you read from the RNG, so it depletes to nothing after a finite number of reads. I've finally come to the conclusion that entropy, whatever that means, is orthogonal to RNGs. Instead, RNGs should be classified by their unpredictability. A CSPRNG is one with high unpredictability. And I've given up on trying to build a DIY HWRNG. It was a misbegotten project.

thanatropism 4y ago

Many people actually want low-discrepancy sequences anyway. https://en.wikipedia.org/wiki/Low-discrepancy_sequence https://en.wikipedia.org/wiki/Low-discrepancy_sequence

beyondCritics 4y ago
Should become more attention.