Pseudorandom number generators (PRNGs) use deterministic mathematical algorithms to produce a sequence of numbers with good statistical properties. However, the sequences of numbers produced fail to achieve true randomness. PRNGs usually start with an arithmetic seed value. The algorithm uses this seed to generate an output value and a new seed, which is used to generate the next value, and so on.

The Java API provides a PRNG, the java.util.Random class. This PRNG is portable and repeatable. Consequently, two instances of the java.util.Random class that are created using the same seed will generate identical sequences of numbers in all Java implementations. Seed values are often reused on application initialization or after every system reboot. In other cases, the seed is derived from the current time obtained from the system clock. An attacker can learn the value of the seed by performing some reconnaissance on the vulnerable target and can then build a lookup table for estimating future seed values.

Consequently, the java.util.Random class must not be used either for security-critical applications or for protecting sensitive data. Use a more secure random number generator, such as the java.security.SecureRandom class.

Noncompliant Code Example

This noncompliant code example uses the insecure java.util.Random class. This class produces an identical sequence of numbers for each given seed value; consequently, the sequence of numbers is predictable.

import java.util.Random;
// ...

Random number = new Random(123L);
//...
for (int i = 0; i < 20; i++) {
  // Generate another random integer in the range [0, 20]
  int n = number.nextInt(21);
  System.out.println(n);
}

Compliant Solution

This compliant solution uses the java.security.SecureRandom class to produce high-quality random numbers:

import java.security.SecureRandom;
import java.security.NoSuchAlgorithmException;
// ...

public static void main (String args[]) {
  SecureRandom number = new SecureRandom();
  // Generate 20 integers 0..20
  for (int i = 0; i < 20; i++) {
    System.out.println(number.nextInt(21));
  }
}

Compliant Solution (Java 8)

This compliant solution uses the SecureRandom.getInstanceStrong() method, introduced in Java 8, to use a strong RNG algorithm, if one is available.

import java.security.SecureRandom;
import java.security.NoSuchAlgorithmException;
// ...

public static void main (String args[]) {
   try {
     SecureRandom number = SecureRandom.getInstanceStrong();
     // Generate 20 integers 0..20
     for (int i = 0; i < 20; i++) {
       System.out.println(number.nextInt(21));
     }
   } catch (NoSuchAlgorithmException nsae) { 
     // Forward to handler
   }
}

Exceptions

MSC02-J-EX0: Using the default constructor for java.util.Random applies a seed value that is "very likely to be distinct from any other invocation of this constructor" [API 2014] and may improve security marginally. As a result, it may be used only for noncritical applications operating on nonsensitive data. Java's default seed uses the system's time in milliseconds. When used, explicit documentation of this exception is required.

import java.util.Random;
// ...

Random number = new Random(); // Used only for demo purposes
int n;
//...
for (int i = 0; i < 20; i++) {
  // Reseed generator
  number = new Random();
  // Generate another random integer in the range [0, 20]
  n = number.nextInt(21);
  System.out.println(n);
}

For noncritical cases, such as adding some randomness to a game or unit testing, the use of class Random is acceptable. However, it is worth reiterating that the resulting low-entropy random numbers are insufficiently random to be used for more security-critical applications, such as cryptography.

MSC02-J-EX1: Predictable sequences of pseudorandom numbers are required in some cases, such as when running regression tests of program behavior. Use of the insecure java.util.Random class is permitted in such cases. However, security-related applications may invoke this exception only for testing purposes; this exception may not be applied in a production context.

Risk Assessment

Predictable random number sequences can weaken the security of critical applications such as cryptography.

Rule

Severity

Likelihood

Remediation Cost

Priority

Level

MSC02-J

High

Probable

Medium

P12

L1

Automated Detection

Tool
Version
Checker
Description
Coverity7.5RISKY_CRYPTOImplemented
Parasoft Jtest SECURITY.WSC.SRDImplemented
SonarQubeS2245 

Related Vulnerabilities

CVE-2006-6969 describes a vulnerability that enables attackers to guess session identifiers, bypass authentication requirements, and conduct cross-site request forgery attacks.

Related Guidelines

SEI CERT C Coding Standard

MSC30-C. Do not use the rand() function for generating pseudorandom numbers

SEI CERT C++ Coding Standard

MSC50-CPP. Do not use std::rand() for generating pseudorandom numbers

MITRE CWE

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

CWE-330, Use of Insufficiently Random Values

CWE-332, Insufficient Entropy in PRNG

CWE-336, Same Seed in PRNG

CWE-337, Predictable Seed in PRNG

Bibliography

 

[API 2014

Class Random
 Class SecureRandom

[FindBugs 2008]

BC. Random objects created and used only once

 

[Monsch 2006]