The product's random number generator produces a series of values which, when observed, can be used to infer a relatively small range of possibilities for the next value that could be generated.

The output of a random number generator should not be predictable based on observations of previous values. In some cases, an attacker cannot predict the exact value that will be produced next, but can narrow down the possibilities significantly. This reduces the amount of effort to perform a brute force attack. For example, suppose the product generates random numbers between 1 and 100, but it always produces a larger value until it reaches 100. If the generator produces an 80, then the attacker knows that the next value will be somewhere between 81 and 100. Instead of 100 possibilities, the attacker only needs to consider 20.

Modes Of Introduction

Architecture and Design
Implementation

Applicable Platforms

Language

Class: Not Language-Specific (Undetermined)

Common Consequences

Potential Mitigations

Increase the entropy used to seed a PRNG.
Phases : Architecture and Design // Requirements
Use products or modules that conform to FIPS 140-2 [REF-267] to avoid obvious entropy problems. Consult FIPS 140-2 Annex C ("Approved Random Number Generators").
Phases : Implementation
Use a PRNG that periodically re-seeds itself using input from high-quality sources, such as hardware devices with high entropy. However, do not re-seed too frequently, or else the entropy source might block.

Vulnerability Mapping Notes

Rationale : This CWE entry is at the Base level of abstraction, which is a preferred level of abstraction for mapping to the root causes of vulnerabilities.
Comments : Carefully read both the name and description to ensure that this mapping is an appropriate fit. Do not try to 'force' a mapping to a lower-level Base/Variant simply to comply with this preferred level of abstraction.

Notes

As of CWE 4.5, terminology related to randomness, entropy, and predictability can vary widely. Within the developer and other communities, "randomness" is used heavily. However, within cryptography, "entropy" is distinct, typically implied as a measurement. There are no commonly-used definitions, even within standards documents and cryptography papers. Future versions of CWE will attempt to define these terms and, if necessary, distinguish between them in ways that are appropriate for different communities but do not reduce the usability of CWE for mapping, understanding, or other scenarios.

References

REF-267

SECURITY REQUIREMENTS FOR CRYPTOGRAPHIC MODULES
Information Technology Laboratory, National Institute of Standards and Technology.
https://csrc.nist.gov/csrc/media/publications/fips/140/2/final/documents/fips1402.pdf

REF-320

Strange Attractors and TCP/IP Sequence Number Analysis
Michal Zalewski.
https://lcamtuf.coredump.cx/oldtcp/tcpseq.html

REF-44

24 Deadly Sins of Software Security
Michael Howard, David LeBlanc, John Viega.

Submission

Modifications