The product uses insufficiently random numbers or values in a security context that depends on unpredictable numbers.

Primary

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.

Architecture and Design

Implementation
REALIZATION: This weakness is caused during implementation of an architectural security tactic.

High

Confidentiality
Other
Other
When a protection mechanism relies on random values to restrict access to a sensitive resource, such as a session ID or a seed for generating a cryptographic key, then the resource being protected could be accessed by guessing the ID or key.

Access Control
Other
Bypass Protection Mechanism
Other
If product relies on unique, unguessable IDs to identify a resource, an attacker might be able to guess an ID for a resource that is owned by another user. The attacker could then read the resource, or pre-create a resource with the same ID to prevent the legitimate program from properly sending the resource to the intended user. For example, a product might maintain session information in a file whose name is based on a username. An attacker could pre-create this file for a victim user, then set the permissions so that the application cannot generate the session for the victim, preventing the victim from using the application.

Access Control
Bypass Protection Mechanism
Gain Privileges or Assume Identity
When an authorization or authentication mechanism relies on random values to restrict access to restricted functionality, such as a session ID or a seed for generating a cryptographic key, then an attacker may access the restricted functionality by guessing the ID or key.

Black Box

Use monitoring tools that examine the software's process as it interacts with the operating system and the network. This technique is useful in cases when source code is unavailable, if the software was not developed by you, or if you want to verify that the build phase did not introduce any new weaknesses. Examples include debuggers that directly attach to the running process; system-call tracing utilities such as truss (Solaris) and strace (Linux); system activity monitors such as FileMon, RegMon, Process Monitor, and other Sysinternals utilities (Windows); and sniffers and protocol analyzers that monitor network traffic.
Attach the monitor to the process and look for library functions that indicate when randomness is being used. Run the process multiple times to see if the seed changes. Look for accesses of devices or equivalent resources that are commonly used for strong (or weak) randomness, such as /dev/urandom on Linux. Look for library or system calls that access predictable information such as process IDs and system time.

Automated Static Analysis - Binary or Bytecode

According to SOAR [REF-1479], the following detection techniques may be useful:

Cost effective for partial coverage:

Bytecode Weakness Analysis - including disassembler + source code weakness analysis
Binary Weakness Analysis - including disassembler + source code weakness analysis

SOAR Partial

Manual Static Analysis - Binary or Bytecode

According to SOAR [REF-1479], the following detection techniques may be useful:

Cost effective for partial coverage:

Binary / Bytecode disassembler - then use manual analysis for vulnerabilities & anomalies

SOAR Partial

Dynamic Analysis with Manual Results Interpretation

According to SOAR [REF-1479], the following detection techniques may be useful:

Cost effective for partial coverage:

Man-in-the-middle attack tool

SOAR Partial

Manual Static Analysis - Source Code

According to SOAR [REF-1479], the following detection techniques may be useful:

Highly cost effective:

Focused Manual Spotcheck - Focused manual analysis of source
Manual Source Code Review (not inspections)

High

Automated Static Analysis - Source Code

According to SOAR [REF-1479], the following detection techniques may be useful:

Cost effective for partial coverage:

Source code Weakness Analyzer
Context-configured Source Code Weakness Analyzer

SOAR Partial

Architecture or Design Review

According to SOAR [REF-1479], the following detection techniques may be useful:

Highly cost effective:

Inspection (IEEE 1028 standard) (can apply to requirements, design, source code, etc.)

High

Architecture and Design

Use a well-vetted algorithm that is currently considered to be strong by experts in the field, and select well-tested implementations with adequate length seeds.
In general, if a pseudo-random number generator is not advertised as being cryptographically secure, then it is probably a statistical PRNG and should not be used in security-sensitive contexts.
Pseudo-random number generators can produce predictable numbers if the generator is known and the seed can be guessed. A 256-bit seed is a good starting point for producing a "random enough" number.

Implementation
Consider a PRNG that re-seeds itself as needed from high quality pseudo-random output sources, such as hardware devices.

Testing
Use automated static analysis tools that target this type of weakness. Many modern techniques use data flow analysis to minimize the number of false positives. This is not a perfect solution, since 100% accuracy and coverage are not feasible.

Architecture and Design
Requirements
Libraries or Frameworks
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").

Testing
Use tools and techniques that require manual (human) analysis, such as penetration testing, threat modeling, and interactive tools that allow the tester to record and modify an active session. These may be more effective than strictly automated techniques. This is especially the case with weaknesses that are related to design and business rules.

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).

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.

CVE-2021-3692
PHP framework uses mt_rand() function (Marsenne Twister) when generating tokens
https://www.cve.org/CVERecord?id=CVE-2021-3692

CVE-2020-7010
Cloud application on Kubernetes generates passwords using a weak random number generator based on deployment time.
https://www.cve.org/CVERecord?id=CVE-2020-7010

CVE-2009-3278
Crypto product uses rand() library function to generate a recovery key, making it easier to conduct brute force attacks.
https://www.cve.org/CVERecord?id=CVE-2009-3278

CVE-2009-3238
Random number generator can repeatedly generate the same value.
https://www.cve.org/CVERecord?id=CVE-2009-3238

CVE-2009-2367
Web application generates predictable session IDs, allowing session hijacking.
https://www.cve.org/CVERecord?id=CVE-2009-2367

CVE-2009-2158
Password recovery utility generates a relatively small number of random passwords, simplifying brute force attacks.
https://www.cve.org/CVERecord?id=CVE-2009-2158

CVE-2009-0255
Cryptographic key created with a seed based on the system time.
https://www.cve.org/CVERecord?id=CVE-2009-0255

CVE-2008-5162
Kernel function does not have a good entropy source just after boot.
https://www.cve.org/CVERecord?id=CVE-2008-5162

CVE-2008-4905
Blogging software uses a hard-coded salt when calculating a password hash.
https://www.cve.org/CVERecord?id=CVE-2008-4905

CVE-2008-4929
Bulletin board application uses insufficiently random names for uploaded files, allowing other users to access private files.
https://www.cve.org/CVERecord?id=CVE-2008-4929

CVE-2008-3612
Handheld device uses predictable TCP sequence numbers, allowing spoofing or hijacking of TCP connections.
https://www.cve.org/CVERecord?id=CVE-2008-3612

CVE-2008-2433
Web management console generates session IDs based on the login time, making it easier to conduct session hijacking.
https://www.cve.org/CVERecord?id=CVE-2008-2433

CVE-2008-0166
SSL library uses a weak random number generator that only generates 65,536 unique keys.
https://www.cve.org/CVERecord?id=CVE-2008-0166

CVE-2008-2108
Chain: insufficient precision causes extra zero bits to be assigned, reducing entropy for an API function that generates random numbers.
https://www.cve.org/CVERecord?id=CVE-2008-2108

CVE-2008-2108
Chain: insufficient precision (CWE-1339) in
random-number generator causes some zero bits to be reliably
generated, reducing the amount of entropy (CWE-331)
https://www.cve.org/CVERecord?id=CVE-2008-2108

CVE-2008-2020
CAPTCHA implementation does not produce enough different images, allowing bypass using a database of all possible checksums.
https://www.cve.org/CVERecord?id=CVE-2008-2020

CVE-2008-0087
DNS client uses predictable DNS transaction IDs, allowing DNS spoofing.
https://www.cve.org/CVERecord?id=CVE-2008-0087

CVE-2008-0141
Application generates passwords that are based on the time of day.
https://www.cve.org/CVERecord?id=CVE-2008-0141

Cryptography
Authentication
Session Management

Randomness and Predictability

Insecure Randomness

A2
Broken Access Control
CWE More Specific

CON33-C
Avoid race conditions when using library functions
Imprecise

MSC30-C
Do not use the rand() function for generating pseudorandom numbers
CWE More Abstract

MSC32-C
Properly seed pseudorandom number generators
CWE More Abstract

11
Brute Force

18
Credential/Session Prediction

MSC02-J
Generate strong random numbers

Discouraged
This CWE entry is a level-1 Class (i.e., a child of a Pillar). It might have lower-level children that would be more appropriate
Examine children of this entry to see if there is a better fit

This can be primary to many other weaknesses such as cryptographic errors, authentication errors, symlink following, information leaks, and others.

As of CWE 4.3, CWE-330 and its descendants are being
investigated by the CWE crypto team to identify gaps
related to randomness and unpredictability, as well as
the relationships between randomness and cryptographic
primitives. This "subtree analysis" might
result in the addition or deprecation of existing
entries; the reorganization of relationships in some
views, e.g. the research view (CWE-1000); more consistent
use of terminology; and/or significant modifications to
related entries.

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.

PLOVER
2006-07-19
Draft 3
2006-07-19

Eric Dalci
Cigital
2008-07-01
updated Time_of_Introduction

CWE Content Team
MITRE
2008-09-08
updated Background_Details, Relationships, Other_Notes, Relationship_Notes, Taxonomy_Mappings, Weakness_Ordinalities

CWE Content Team
MITRE
2008-11-24
updated Relationships, Taxonomy_Mappings

CWE Content Team
MITRE
2009-01-12
updated Description, Likelihood_of_Exploit, Other_Notes, Potential_Mitigations, Relationships

CWE Content Team
MITRE
2009-03-10
updated Potential_Mitigations

CWE Content Team
MITRE
2009-05-27
updated Demonstrative_Examples, Related_Attack_Patterns

CWE Content Team
MITRE
2009-12-28
updated Applicable_Platforms, Common_Consequences, Description, Observed_Examples, Potential_Mitigations, Time_of_Introduction

CWE Content Team
MITRE
2010-02-16
updated References, Relationships, Taxonomy_Mappings

CWE Content Team
MITRE
2010-04-05
updated Related_Attack_Patterns

CWE Content Team
MITRE
2010-06-21
updated Detection_Factors, Potential_Mitigations

CWE Content Team
MITRE
2011-03-29
updated Demonstrative_Examples

CWE Content Team
MITRE
2011-06-01
updated Common_Consequences, Relationships, Taxonomy_Mappings

CWE Content Team
MITRE
2011-06-27
updated Relationships

CWE Content Team
MITRE
2011-09-13
updated Potential_Mitigations, References, Relationships, Taxonomy_Mappings

CWE Content Team
MITRE
2012-05-11
updated Demonstrative_Examples, Observed_Examples, References, Relationships

CWE Content Team
MITRE
2014-02-18
updated Related_Attack_Patterns

CWE Content Team
MITRE
2014-06-23
updated Related_Attack_Patterns

CWE Content Team
MITRE
2014-07-30
updated Detection_Factors

CWE Content Team
MITRE
2015-12-07
updated Relationships

CWE Content Team
MITRE
2017-11-08
updated Functional_Areas, Likelihood_of_Exploit, Modes_of_Introduction, References, Relationships, Taxonomy_Mappings

CWE Content Team
MITRE
2018-03-27
updated References

CWE Content Team
MITRE
2019-01-03
updated Relationships, Taxonomy_Mappings

CWE Content Team
MITRE
2019-06-20
updated Relationships

CWE Content Team
MITRE
2020-02-24
updated Applicable_Platforms, Description, Relationships

CWE Content Team
MITRE
2021-03-15
updated Maintenance_Notes, Relationships

CWE Content Team
MITRE
2021-07-20
updated Demonstrative_Examples, Maintenance_Notes, Observed_Examples

CWE Content Team
MITRE
2021-10-28
updated Relationships

CWE Content Team
MITRE
2022-10-13
updated Observed_Examples, Relationships

CWE Content Team
MITRE
2023-01-31
updated Common_Consequences, Description

CWE Content Team
MITRE
2023-04-27
updated References, Relationships

CWE Content Team
MITRE
2023-06-29
updated Mapping_Notes, Relationships

CWE Content Team
MITRE
2023-10-26
updated Observed_Examples

CWE Content Team
MITRE
2024-02-29
4.14
2024-02-29
updated Mapping_Notes

CWE Content Team
MITRE
2025-09-09
4.18
2025-09-09
updated Description, Detection_Factors, Diagram, References

CWE Content Team
MITRE
2025-12-11
4.19
2025-12-11
updated Relationships

Randomness and Predictability