CertC++-MSC32ΒΆ

Properly seed pseudorandom number generators

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 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 (POSIX)

This noncompliant code example generates a sequence of 10 pseudorandom numbers using the random() function. When random() is not seeded, it behaves like rand(), producing the same sequence of random numbers each time any program that uses it is run. 

#include <stdio.h>
#include <stdlib.h>
 
void func(void) {
  for (unsigned int i = 0; i < 10; ++i) {
    /* Always generates the same sequence */
    printf("%ld, ", random());
  }
}

The output is as follows:

1st run: 1804289383, 846930886, 1681692777, 1714636915, 1957747793, 424238335, 719885386, 1649760492, 596516649,
         1189641421,
2nd run: 1804289383, 846930886, 1681692777, 1714636915, 1957747793, 424238335, 719885386, 1649760492, 596516649,
         1189641421,
...
nth run: 1804289383, 846930886, 1681692777, 1714636915, 1957747793, 424238335, 719885386, 1649760492, 596516649,
         1189641421,
Compliant Solution (POSIX)

Call srandom() before invoking random() to seed the random sequence generated by random(). This compliant solution produces different random number sequences each time the function is called, depending on the resolution of the system clock: 

#include <stdio.h>
#include <stdlib.h>
#include <time.h>
 
void func(void) {
  struct timespec ts;
  if (timespec_get(&ts, TIME_UTC) == 0) {
    /* Handle error */
  } else {
    srandom(ts.tv_nsec ^ ts.tv_sec);
    for (unsigned int i = 0; i < 10; ++i) {
      /* Generates different sequences at different runs */
      printf("%ld, ", random());
    }
  }
}

The output is as follows:

1st run: 198682410, 2076262355, 910374899, 428635843, 2084827500, 1558698420, 4459146, 733695321, 2044378618, 1649046624,
2nd run: 1127071427, 252907983, 1358798372, 2101446505, 1514711759, 229790273, 954268511, 1116446419, 368192457,
         1297948050,
3rd run: 2052868434, 1645663878, 731874735, 1624006793, 938447420, 1046134947, 1901136083, 418123888, 836428296,
         2017467418,

This may not be sufficiently random for concurrent execution, which may lead to correlated generated series in different threads. Depending on the application and the desired level of security, a programmer may choose alternative ways to seed PRNGs. In general, hardware is more capable than software of generating real random numbers (for example, by sampling the thermal noise of a diode).

Compliant Solution (Windows)

The  BCryptGenRandom() function does not run the risk of not being properly seeded because its arguments serve as seeders: 

#include <stdio.h>
#include <Windows.h>
#include <Bcrypt.h>
#include <Ntstatus.h>
#include <Wincrypt.h>

void func(void) {
  BCRYPT_ALG_HANDLE hAlgorithm = NULL;
  long rand_buf;
  PUCHAR pbBuffer = (PUCHAR) &rand_buf;
  ULONG cbBuffer = sizeof(rand_buf);
  ULONG dwFlags = BCRYPT_USE_SYSTEM_PREFERRED_RNG;
  NTSTATUS status;
  for (unsigned int i = 0; i < 10; ++i) {
    status = BCryptGenRandom(hAlgorithm, pbBuffer, cbBuffer, dwFlags);
    if (status == STATUS_SUCCESS) {
      printf("%ld, ", rand_buf);
    } else {
      /* Handle Error */
    }
  }
}

The output is as follows:

1st run: -683378946, 1957231690, 1933176011, -1745403355, -883473417, 882992405, 169629816, 1824800038, 899851668, 1702784647,
2nd run: -58750553, -1921870721, -1973269161, 1512649964, -673518452, 234003619, -1622633366, 1312389688, -2125631172, 2067680022,
3rd run: -189899579, 1220698973, 752205360, -1826365616, 79310867, 1430950090, -283206168, -941773185, 129633665, 543448789,
Risk Assessment
Rule Severity Likelihood Remediation Cost Priority Level
MSC32-C Medium Likely Low P18 L1
Related Guidelines
Taxonomy Taxonomy item Relationship
CERT C Secure Coding Standard MSC30-C. Do not use the rand() function for generating pseudorandom numbers Prior to 2018-01-12: CERT: Unspecified Relationship
CERT C MSC51-CPP. Ensure your random number generator is properly seeded Prior to 2018-01-12: CERT: Unspecified Relationship
CWE 2.11 CWE-327, Use of a Broken or Risky Cryptographic Algorithm 2017-05-16: CERT: Rule subset of CWE
CWE 2.11 CWE-330, Use of Insufficiently Random Values 2017-06-28: CERT: Rule subset of CWE
CWE 2.11 CWE-331, Insufficient Entropy 2017-06-28: CERT: Exact
Bibliography
[ MSDN] " BCryptGenRandom() Function"
Excerpt from SEI CERT C++ Coding Standard [https://cmu-sei.github.io/secure-coding-standards/sei-cert-c-coding-standard/rules/miscellaneous-msc/msc32-c], 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

seed_properly

Properly seed pseudorandom number generators.

None

False

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