# Difference between revisions of "Insecure Randomness"

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There are two types of PRNGs: statistical and cryptographic. Statistical PRNGs provide useful statistical properties, but their output is highly predictable and forms an easy to reproduce numeric stream that is unsuitable for use in cases where security depends on generated values being unpredictable. Cryptographic PRNGs address this problem by generating output that is more difficult to predict. For a value to be cryptographically secure, it must be impossible or highly improbable for an attacker to distinguish between it and a truly random value. In general, if a PRNG algorithm is not advertised as being cryptographically secure, then it is probably a statistical PRNG and should not be used in security-sensitive contexts. | There are two types of PRNGs: statistical and cryptographic. Statistical PRNGs provide useful statistical properties, but their output is highly predictable and forms an easy to reproduce numeric stream that is unsuitable for use in cases where security depends on generated values being unpredictable. Cryptographic PRNGs address this problem by generating output that is more difficult to predict. For a value to be cryptographically secure, it must be impossible or highly improbable for an attacker to distinguish between it and a truly random value. In general, if a PRNG algorithm is not advertised as being cryptographically secure, then it is probably a statistical PRNG and should not be used in security-sensitive contexts. | ||

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__NOTOC__ | __NOTOC__ |

## Revision as of 10:44, 20 December 2013

*This is a Vulnerability. To view all vulnerabilities, please see the Vulnerability Category page.*

Last revision (mm/dd/yy): **12/20/2013**

Vulnerabilities Table of Contents

## Description

Standard pseudo-random number generators cannot withstand cryptographic attacks.

Insecure randomness errors occur when a function that can produce predictable values is used as a source of randomness in security-sensitive context.

Computers are deterministic machines, and as such are unable to produce true randomness. Pseudo-Random Number Generators (PRNGs) approximate randomness algorithmically, starting with a seed from which subsequent values are calculated.

There are two types of PRNGs: statistical and cryptographic. Statistical PRNGs provide useful statistical properties, but their output is highly predictable and forms an easy to reproduce numeric stream that is unsuitable for use in cases where security depends on generated values being unpredictable. Cryptographic PRNGs address this problem by generating output that is more difficult to predict. For a value to be cryptographically secure, it must be impossible or highly improbable for an attacker to distinguish between it and a truly random value. In general, if a PRNG algorithm is not advertised as being cryptographically secure, then it is probably a statistical PRNG and should not be used in security-sensitive contexts.

## Examples

The following code uses a statistical PRNG to create a URL for a receipt that remains active for some period of time after a purchase (DO NOT DO THIS).

String GenerateReceiptURL(String baseUrl) { Random ranGen = new Random(); ranGen.setSeed((new Date()).getTime()); return(baseUrl + Gen.nextInt(400000000) + ".html"); }

This code uses the Random.nextInt() function to generate "unique" identifiers for the receipt pages it generates. Because Random.nextInt() is a statistical PRNG, it is easy for an attacker to guess the strings it generates. Although the underlying design of the receipt system is also faulty, it would be more secure if it used a random number generator that did not produce predictable receipt identifiers, such as a cryptographic PRNG.

The following code uses Java's SecureRandom class to generate a cryptographically strong pseudo-random number (DO THIS):

public static int generateRandom(int maximumValue) { SecureRandom ranGen = new SecureRandom(); return ranGen.nextInt(maximumValue); }