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Pseudorandom number generators use mathematical algorithms to produce a sequence of numbers with good statistical properties, but the numbers produced are not genuinely random.

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.

Noncompliant Code Example

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 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);
  // ...
}

Compliant Solution

The C++ standard library provides mechanisms for fine-grained control over pseudorandom number generation. It breaks random number generation into two parts: one 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));
  // ...
}

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.

Rule

Severity

Likelihood

Remediation Cost

Priority

Level

MSC50-CPP

Medium

Unlikely

Low

P6

L2

Automated Detection

Tool

Version

Checker

Description

Axivion Bauhaus Suite

6.9.0

CertC++-MSC50
Clang
4.0 (prerelease)
cert-msc50-cppChecked by clang-tidy
CodeSonar
5.1p0
BADFUNC.RANDOM.RANDUse of rand
Compass/ROSE




ECLAIR

1.2

CC2.MSC30

Fully implemented

LDRA tool suite
9.7.1

 

44 S

Enhanced Enforcement

Parasoft C/C++test
10.4.2
CERT_CPP-MSC50-a

Do not use the rand() function for generating pseudorandom numbers

Polyspace Bug Finder

R2019a

CERT C++: MSC50-CPPChecks for use of vulnerable pseudo-random number generator (rule partially covered)
PRQA QA-C++
4.3
5028Fully implemented

Related Vulnerabilities

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

Related Guidelines

Bibliography

[ISO/IEC 9899:2011]Subclause 7.22.2, "Pseudo-random Sequence Generation Functions"
[ISO/IEC 14882-2014]Subclause 26.5, "Random Number Generation"