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The C Standard rand() function, exposed through the C++ standard library through <cstdlib> as std::rand(), makes no guarantees as to the quality of the random sequence produced. The numbers generated by some implementations of std::rand() have a comparatively short cycle, and the numbers can be predictable. Applications that have strong pseudorandom number requirements must use a generator that is known to be sufficient for their needs.

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The following noncompliant code generates an ID with a numeric part produced by calling the rand() function. The IDs produced are predictable and have limited randomness. Further, depending on the value of RAND_MAX, the resulting value has can have modulo bias.

#include <cstdlib>
#include <string>
 
void f() {
  std::string id("ID"); // Holds the ID, starting with the characters "ID" followed
                        // by a random integer in the range [0-10000].
  id += std::to_string(std::rand() % 10000);
  // ...
}

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The C++ standard library provides mechanisms for fine-grained control over pseudorandom number generation. It breaks random number generation down into two parts: one part is the algorithm responsible for providing random values (the engine), and the other is responsible for distribution of the random values via a density function (the distribution). The distribution object is not strictly required, but it works to ensure that values are properly distributed within a given range instead of improperly distributed due to bias issues. This compliant solution uses the Mersenne Twister algorithm as the engine for generating random values and a uniform distribution to negate the modulo bias from the noncompliant code example:.

#include <random>
#include <string>
 
void f() {
  std::string id("ID"); // Holds the ID, starting with the characters "ID" followed
                        // by a random integer in the range [0-10000].
  std::uniform_int_distribution<int> distribution(0, 10000);
  std::random_device rd;
  std::mt19937 engine(rd());
  id += std::to_string(distribution(engine));
  // ...
}

Note that this This compliant solution also seeds the random number engine, in conformance with MSC51-CPP. Ensure your random number generator is properly seeded.

Risk Assessment

Using the std::rand() function could lead to predictable random numbers.

Automated Detection

Related Vulnerabilities

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

Related Guidelines

Bibliography

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[ISO/IEC 9899:2011]

Subclause 7.22.2, "Pseudo-random Sequence Generation Functions"

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[ISO/IEC 14882-2014]	Subclause 26.5, "Random Number Generation"

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