CWE-338: Use of Cryptographically Weak Pseudo-Random Number Generator (PRNG) (Base)

Description: The product uses a Pseudo-Random Number Generator (PRNG) in a security context, but the PRNG's algorithm is not cryptographically strong.

Extended Description: When a non-cryptographic PRNG is used in a cryptographic context, it can expose the cryptography to certain types of attacks. Often a pseudo-random number generator (PRNG) is not designed for cryptography. Sometimes a mediocre source of randomness is sufficient or preferable for algorithms that use random numbers. Weak generators generally take less processing power and/or do not use the precious, finite, entropy sources on a system. While such PRNGs might have very useful features, these same features could be used to break the cryptography.



Applicable Platforms: ::LANGUAGE CLASS:Not Language-Specific:LANGUAGE PREVALENCE:Undetermined::

Modes of Introduction: ::PHASE:Architecture and Design::PHASE:Implementation:NOTE:REALIZATION: This weakness is caused during implementation of an architectural security tactic.::



Common Consequences: ::SCOPE:Access Control:IMPACT:Bypass Protection Mechanism:NOTE:If a PRNG is used for authentication and authorization, such as a session ID or a seed for generating a cryptographic key, then an attacker may be able to easily guess the ID or cryptographic key and gain access to restricted functionality.::

Detection Methods: ::METHOD:Automated Static Analysis:DESCRIPTION:Automated static analysis, commonly referred to as Static Application Security Testing (SAST), can find some instances of this weakness by analyzing source code (or binary/compiled code) without having to execute it. Typically, this is done by building a model of data flow and control flow, then searching for potentially-vulnerable patterns that connect sources (origins of input) with sinks (destinations where the data interacts with external components, a lower layer such as the OS, etc.):EFFECTIVENESS:High::

Potential Mitigations: ::PHASE:Implementation:DESCRIPTION:Use functions or hardware which use a hardware-based random number generation for all crypto. This is the recommended solution. Use CyptGenRandom on Windows, or hw_rand() on Linux.::

Observed Examples: ::REFERENCE:CVE-2021-3692:DESCRIPTION:PHP framework uses mt_rand() function (Marsenne Twister) when generating tokens:LINK:https://www.cve.org/CVERecord?id=CVE-2021-3692::REFERENCE:CVE-2009-3278:DESCRIPTION:Crypto product uses rand() library function to generate a recovery key, making it easier to conduct brute force attacks.:LINK:https://www.cve.org/CVERecord?id=CVE-2009-3278::REFERENCE:CVE-2009-3238:DESCRIPTION:Random number generator can repeatedly generate the same value.:LINK:https://www.cve.org/CVERecord?id=CVE-2009-3238::REFERENCE:CVE-2009-2367:DESCRIPTION:Web application generates predictable session IDs, allowing session hijacking.:LINK:https://www.cve.org/CVERecord?id=CVE-2009-2367::REFERENCE:CVE-2008-0166:DESCRIPTION:SSL library uses a weak random number generator that only generates 65,536 unique keys.:LINK:https://www.cve.org/CVERecord?id=CVE-2008-0166::



Notes: ::TYPE:Maintenance:NOTE: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.::