CertC++-MSC51

Ensure your random number generator is properly seeded

Required inputs: IR

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 a constant 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. If the PRNG is subsequently seeded with the same initial seed value, then it will generate the same sequence.

Consequently, 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 a PRNG is always properly seeded with an initial seed value that will not be predictable or controllable by an attacker. 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 PRNGs 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() << ", ";
  }
}

The output of this example follows.

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,
Noncompliant Code Example

This noncompliant code example improves the previous noncompliant code example by seeding the random number generation engine with the current time. However, this approach is still unsuitable when an attacker can control the time at which the seeding is executed. Predictable seed values can result in exploits when the subverted PRNG is used. 

#include <ctime>
#include <random>
#include <iostream>

void f() {
  std::time_t t;
  std::mt19937 engine(std::time(&t));

  for (int i = 0; i < 10; ++i) {
    std::cout << engine() << ", ";
  }
}
Compliant Solution

This compliant solution uses  std::random_device to generate a random value for seeding the Mersenne Twister engine object. The values generated by  std::random_device are nondeterministic random numbers when possible, relying on random number generation devices, such as /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() << ", ";
  }
} 

The output of this example follows.

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
Rule Severity Likelihood Remediation Cost Priority Level
MSC51-CPP Medium Likely Low P18 L1
Related Guidelines
SEI CERT C Coding Standard MSC32-C. Properly seed pseudorandom number generators
MITRE CWE

CWE-327, Use of a Broken or Risky Cryptographic Algorithm

CWE-330, Use of Insufficiently Random Values

CWE-337, Predictable Seed in PRNG
Bibliography
[ ISO/IEC 9899:2011] Subclause 7.22.2, "Pseudo-random Sequence Generation Functions"
[ ISO/IEC 14882-2014] Subclause 26.5, "Random Number Generation"
Excerpt from SEI CERT C++ Coding Standard [https://cmu-sei.github.io/secure-coding-standards/sei-cert-cpp-coding-standard/rules/miscellaneous-msc/msc51-cpp], Copyright (C) 1995-2026 Carnegie Mellon University. See section 9.4. "3rd-Party Licenses" in the documentation for full details.

Possible Messages

Key

Text

Severity

Disabled

forbidden_default_constructor_call

Call to forbidden default constructor.

None

False

forbidden_seed

Random number generation engine may be initialized with non-compliant seed.

None

False

Options

blacklist

blacklist

Type: set[bauhaus.analysis.config.QualifiedName]

Default: {'std::default_random_engine', 'std::knuth_b', 'std::linear_congruential_engine', 'std::mersenne_twister_engine', 'std::minstd_rand0', 'std::mt19937', 'std::mt19937_64', 'std::ranlux24', 'std::ranlux24_base', 'std::ranlux48', 'std::ranlux48_base', 'std::subtract_with_carry_engine'}

Set of class names where calling the default constructor is forbidden.
 

proper_seed_classes

proper_seed_classes : set[bauhaus.analysis.config.QualifiedName] = {'std::random_device'}

Set of acceptable seed class names.
 

random_generators

random_generators

Type: set[bauhaus.analysis.config.QualifiedName]

Default: {'std::default_random_engine', 'std::knuth_b', 'std::linear_congruential_engine', 'std::mersenne_twister_engine', 'std::minstd_rand0', 'std::mt19937', 'std::mt19937_64', 'std::ranlux24', 'std::ranlux24_base', 'std::ranlux48', 'std::ranlux48_base', 'std::subtract_with_carry_engine'}

List of random generator classes that need to be initialized with a proper seed.