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Calling a random 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 RNG) that is not seeded will result in generating the same sequence of random numbers in different runs of the program. Suppose there is code that calls an RNG function 10 times Consider a PRNG function that is seeded with some initial seed value and is consecutively called to produce a sequence of 10 random numbers. Suppose, also, that this RNG is not seeded. Running the code the first time will produce the sequence S = <r1, r2, r3, r4, r5, r6, r7, r8, r9, r10>. Running the code again a second time will produce the exact same sequence S. Generally, any subsequent runs of the code , S. If the PRNG is subsequently seeded with the same initial seed value, then it will generate the same sequence sequence S.

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

As a solution, you should always ensure that your RNG is properly seeded. Seeding an RNG means that it will generate different sequences of random numbers at any call.

Rule MSC30-CPP. Do not use the rand() function for generating pseudorandom numbers addresses RNGs from a different perspective, i.e., the time until the first collision occurs. In other words, during a single run of an RNG, the time interval after which the RNG generates the same random numbers. Rule MSC30-CPP deprecates the rand() function because it generates numbers which have a comparatively short cycle. The same rule proposes the use of the random() function for POSIX and the CryptGenRandom() function for Windows.

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 The current rule (MSC32-CPP) examines these three RNGs in terms of seeding. Noncompliant code examples correspond to the use of an RNG without a seed, while compliant solutions correspond to the same RNG being properly seeded. Rule MSC32-CPP addresses all three RNGs mentioned in rule MSC30-CPP for completeness. Rule MSC32-CPP complies to MSC30-CPP and does not recommend the use of the rand() function. Nevertheless, if it is unavoidable to use rand(), it should at least be properly seeded.

Noncompliant Code Example

This noncompliant code example generates a sequence of 10 pseudorandom numbers using the rand() function. When rand() is not seeded, it uses 1 as a default seed 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>
int i=0;
for (i=0; i<10; i++#include <iostream>

void f() {
    cout<<rand()<<", "; /* Always generates the same sequence */
}
output:
1st run: 41, 18467, 6334, 26500, 19169, 15724, 11478, 29358, 26962, 24464,
2nd run: 41, 18467, 6334, 26500, 19169, 15724, 11478, 29358, 26962, 24464,
...
nth run: 41, 18467, 6334, 26500, 19169, 15724, 11478, 29358, 26962, 24464,

Compliant Solution (C Standard)

Use srand() before rand() to seed the random sequence generated by rand(). The code produces different random number sequences at different calls.

srand(time(NULL)); /* Create seed based on current time */
int i=0;
for (i=0; i<10; i++std::mt19937 engine;
  
  for (int i = 0; i < 10; ++i) {
    cout<<rand()<<", "; /* Generates different sequences at different runs */
}

output:
1st run: 25121, 15571, 29839, 2454, 6844, 10186, 27534, 6693, 12456, 5756,
2nd run: 25134, 25796, 2992, 403, 15334, 25893, 7216, 27752, 12966, 13931,
3rd run: 25503, 27950, 22795, 32582, 1233, 10862, 31243, 24650, 11000, 7328,
...

Noncompliant Code Example

This noncompliant code example generates a sequence of 10 pseudorandom numbers using the random() function. When random() is not seeded, it behaves like rand() and thus produces the same sequence of random numbers at different calls.

int i=0;
for (i=0; i<10; i++) {
    cout<<random()<<std::cout << engine() << ", ";
 /* Always generates the same sequence */ }
}
 
output:
1st run: 18042893833499211612, 846930886581869302, 16816927773890346734, 17146369153586334585, 1957747793545404204, 4242383354161255391, 7198853863922919429, 1649760492949333985, 5965166492715962298, 11896414211323567403, 
2nd run: 18042893833499211612, 846930886581869302, 16816927773890346734, 17146369153586334585, 1957747793545404204, 4242383354161255391, 7198853863922919429, 1649760492949333985, 5965166492715962298, 11896414211323567403, 
...
nth run: 18042893833499211612, 846930886581869302, 16816927773890346734, 17146369153586334585, 1957747793545404204, 4242383354161255391, 7198853863922919429, 1649760492949333985, 5965166492715962298, 11896414211323567403, 

Compliant Solution

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Use srandom() before random() to seed the random sequence generated by random(). The code produces different random number sequences at different calls.

srandom(time(NULL)); /* Create seed based on current time counted as seconds from 01/01/1970 */
int i=0;
for (i=0; i<10; i++) {
    cout<<random()<<", "; /* Generates different sequences at different runs */
}

output:
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,
...

In the previous examples, seeding in rand() and random() is done using the time() function, which returns the current time calculated as the number of seconds that have past since 01/01/1970. Depending on the application and the desirable level of security, a programmer may choose alternative ways to seed RNGs. In general, hardware is more capable of generating real random numbers (for example, generate a sequence of bits by sampling the thermal noise of a diode and use this as a seed).

Compliant Solution (Windows)

The CryptGenRandom()does not run the risk of not being properly seeded. The reason for that is that its arguments serve as seeders. From the Microsoft Developer Network CryptGenRandom() reference [MSDN]:

The CryptGenRandom function fills a buffer with cryptographically random bytes.

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Syntax

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BOOL WINAPI CryptGenRandom(
__in HCRYPTPROV hProv,
__in DWORD dwLen,
__inout BYTE *pbBuffer
);

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Parameters

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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.

Code Block
bgColor #ccccff lang cpp
#include <random> HCRYPTPROV hCryptProv;#include <iostream> union /* union stores the random number generated by CryptGenRandomvoid f() */ { BYTE bs[sizeof(long int)]std::random_device dev; long int li; } rand_buf; if(CryptAcquireContext(&hCryptProv, NULL, NULL, PROV_RSA_FULL, 0)) /* An example of instantiating the CSP */ { cout<<"CryptAcquireContext succeeded."<<endl; } else { cerr<<"Error during CryptAcquireContext!"<<endl; } for(int i=0;i<10;i++) { if (!CryptGenRandom(hCryptProv, sizeof(rand_buf), (BYTE*) &rand_buf)) { cerr<<"Error during CryptGenRandom"<<endl; } else { cout<<rand_buf.li<<", "; std::mt19937 engine(dev()); for (int i = 0; i < 10; ++i) { std::cout << engine() << ", "; } } output: 1st run: -15978373113921124303, 9061306821253168518, -13080318861183339582, 1048837407197772533, -93104190083186419, -6581146132599073270, -17092209533238222340, -1019697289101548389, 1802206541296330365, 4065058413335314032, 2nd run: 8859041192392369099, -6873795562509898672, -17822968542135685437, 14437019163733236524, -624291047883966369, 20496926922529945396, -990451563764222328, -142307804138530885, 12570792114209173263, 8971851041693483251, 3rd run: 190598304914243768, -15374094642191798381, 15941747392961426773, -4244019163791073717, -19751534742222867426, 8269129271092675429, 17055495952202201605, -1515331215850375565, 4749513993622398137, 1982500583422940882, ...

Risk Assessment

Automated Detection

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Tool	Version	Checker	Description

Related Vulnerabilities

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

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Related Guidelines

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Bibliography

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