MSC32-CPP. Ensure your random number generator is properly seeded

A pseudorandom number generator (PRNG) is a deterministic algorithm capable of generating sequences of numbers that approximate the properties of random numbers. Each sequence is completely determined by the initial state of the PRNG and the algorithm for changing the state. Most PRNGs make it possible to set the initial state, also called the seed state. Setting the initial state is called seeding the PRNG.

Calling a PRNG in the same initial state, either without seeding it explicitly or by seeding it with the same value, results in generating the same sequence of random numbers in different runs of the program. Consider a PRNG function that is seeded with some initial seed value and is consecutively called to produce a sequence of random numbers, S. If the PRNG is subsequently seeded with the same initial seed value, then it will generate the same sequence S.

As a result, after the first run of an improperly seeded PRNG, an attacker can predict the sequence of random numbers that will be generated in the future runs. Improperly seeding or failing to seed the PRNG can lead to vulnerabilities, especially in security protocols.

The solution is to ensure that the PRNG is always properly seeded. A properly seeded PRNG will generate a different sequence of random numbers each time it is run.

Not all random number generators can be seeded. True random number generators that rely on hardware to produce completely unpredictable results do not need to be and cannot be seeded. Some high-quality PRNGs, such as the /dev/random device on some UNIX systems, also cannot be seeded. This rule applies only to algorithmic pseudorandom number generators that can be seeded.

Noncompliant Code Example

This noncompliant code example generates a sequence of 10 pseudorandom numbers using the Mersenne Twister engine. No matter how many times this code is executed, it always produces the same sequence because the default seed is used for the engine:

#include <random>
#include <iostream>

void f() {
  std::mt19937 engine;
  
  for (int i = 0; i < 10; ++i) {
    std::cout << engine() << ", ";
  }
}
 
output:
1st run: 3499211612, 581869302, 3890346734, 3586334585, 545404204, 4161255391, 3922919429, 949333985, 2715962298, 1323567403, 
2nd run: 3499211612, 581869302, 3890346734, 3586334585, 545404204, 4161255391, 3922919429, 949333985, 2715962298, 1323567403, 
...
nth run: 3499211612, 581869302, 3890346734, 3586334585, 545404204, 4161255391, 3922919429, 949333985, 2715962298, 1323567403, 

Compliant Solution

This compliant solution uses std::random_device to generate a random seed value to see the Mersenne Twister engine object. The values generated by std::random_device are nondeterministic random numbers when possible, relying on random number generation devices, like /dev/random. When such a device is not available, std::random_device may employ a random number engine; however, the initial value generated should have sufficient randomness to serve as a seed value.

#include <random>
#include <iostream>

void f() {
  std::random_device dev;
  std::mt19937 engine(dev());
  
  for (int i = 0; i < 10; ++i) {
    std::cout << engine() << ", ";
  }
}

output:
1st run: 3921124303, 1253168518, 1183339582, 197772533, 83186419, 2599073270, 3238222340, 101548389, 296330365, 3335314032, 
2nd run: 2392369099, 2509898672, 2135685437, 3733236524, 883966369, 2529945396, 764222328, 138530885, 4209173263, 1693483251, 
3rd run: 914243768, 2191798381, 2961426773, 3791073717, 2222867426, 1092675429, 2202201605, 850375565, 3622398137, 422940882,
...

Risk Assessment

Automated Detection

Related Vulnerabilities

Search for vulnerabilities resulting from the violation of this rule on the CERT website.

Related Guidelines

Bibliography