Use of Insufficiently Random Values

Description

When product generates predictable values in a context requiring unpredictability, it may be possible for an attacker to guess the next value that will be generated, and use this guess to impersonate another user or access sensitive information.

Background

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.

Demonstrations

The following examples help to illustrate the nature of this weakness and describe methods or techniques which can be used to mitigate the risk.

Note that the examples here are by no means exhaustive and any given weakness may have many subtle varieties, each of which may require different detection methods or runtime controls.

Example One

This code attempts to generate a unique random identifier for a user's session.

function generateSessionID($userID){
  srand($userID);
  return rand();
}

Because the seed for the PRNG is always the user's ID, the session ID will always be the same. An attacker could thus predict any user's session ID and potentially hijack the session.

This example also exhibits a Small Seed Space (CWE-339).

Example Two

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.

String GenerateReceiptURL(String baseUrl) {
  Random ranGen = new Random();
  ranGen.setSeed((new Date()).getTime());
  return(baseUrl + ranGen.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.

See Also