Randomness and Entropy in Node and Electron

Randomness is a hard problem for computers. For this reason most functions that generate randomness are not considered cryptographically secure. That means that it is possible that an attacker can take a good guess at what number a non-secure randomness generator generated.

How Can Randomness Be Attacked?

Many non-secure randomness (or entropy) generators would do something similar to the following:

function getRandom(timestamp, maxNumber){
  // Take the deterministic hash of the timestamp
  const hashedTime = sha256(timestamp)
  // Reduce the hash to within the range [0, maxNumber)
  return hashedTime % maxNumber

This function (while ignoring some implementation details of modulus math by such a large number) will return random numbers that are based on the timestamp input, which is called the seed. If I pass in many different timestamps, the various outputs would appear random. This is an example of a weak pseudo-random number generator.

A weak pseudo-random number generator works perfectly fine if one is trying to:

  • Create sample data for an application
  • Write a video game engine
  • etc …

However, weak pseudo-randomness can be catastrophically dangerous if one is trying to:

  • Generate Bitcoin keys
  • Generate passwords or salts
  • etc …

Strong Psuedo-Randomness (Cryptographically Secure)

A software-only system like Qvault can at best generate strong pseudo-random data because we are working on deterministic systems. Without an outside source of entropy (like someone rolling dice and telling the computer each output), we are at the mercy of pseudo-randomness.



Node’s built-in crypto.randomBytes is a cryptographically secure random number generator that is based on openssl. Depending on the operating system of the user, randomBytes will use

/dev/urandom (unix)


CryptoGenRandom (windows)

While still pseudo-random sources, the important thing is that they are not guessable by an attacker. In other words, after using crypto.randomBytes() to generate a recovery code in Qvault, an attacker can’t recreate that code.

What Do I Do?

In short, use crypto.randomBytes() whenever you need raw random bytes. If you need a random number within a range, for example, a random number between 0-9, then use a non-biased function that uses crypto.randomBytes() as the source of entropy. For example:


Thanks For Reading!

If you’re interested in furthering your CS career, take our computer science courses

Follow and hit us up on Twitter @q_vault if you have any questions or comments, and if we’ve made a mistake be sure to let us know so we can get it corrected!

Subscribe to our newsletter for more programming articles

2 thoughts on “Randomness and Entropy in Node and Electron”

  1. Salts are public information. What’s wrong if an attacker could predict future salts … ? How could they use that for anything malicious?

    Or is there something more to do with it than to just predict future salts. Wouldn’t a PRNG be *good enough* for salting password/passphrases?

    It appears obvious to me we shouldn’t use an incremental value: 1, 2, 3, … as a salt because that is too easily predictable. But if you have a PRNG with a decent output length, that is randomized somewhat, I don’t see how an attacker could compute rainbow tables for it practically.

    • For salts I agree it is less important, however for things like generating passwords/keys unpredictable entropy is a must.

Comments are closed.

%d bloggers like this: