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ID

CWE-337: Predictable Seed in Pseudo-Random Number Generator (PRNG)

Weakness ID: 337
Abstraction: Base
Structure: Simple
Status: Draft
Presentation Filter:
+ Description
A Pseudo-Random Number Generator (PRNG) is initialized from a predictable seed, such as the process ID or system time.
+ Extended Description
The use of predictable seeds significantly reduces the number of possible seeds that an attacker would need to test in order to predict which random numnbers will be generated by the PRNG.
+ Relationships

The table(s) below shows the weaknesses and high level categories that are related to this weakness. These relationships are defined as ChildOf, ParentOf, MemberOf and give insight to similar items that may exist at higher and lower levels of abstraction. In addition, relationships such as PeerOf and CanAlsoBe are defined to show similar weaknesses that the user may want to explore.

+ Relevant to the view "Research Concepts" (CWE-1000)
+ Relevant to the view "Architectural Concepts" (CWE-1008)
NatureTypeIDName
MemberOfCategoryCategory1013Encrypt Data
+ Relevant to the view "Development Concepts" (CWE-699)
+ Modes Of Introduction

The different Modes of Introduction provide information about how and when this weakness may be introduced. The Phase identifies a point in the software life cycle at which introduction may occur, while the Note provides a typical scenario related to introduction during the given phase.

PhaseNote
Architecture and Design
ImplementationREALIZATION: This weakness is caused during implementation of an architectural security tactic.
+ Applicable Platforms
The listings below show possible areas for which the given weakness could appear. These may be for specific named Languages, Operating Systems, Architectures, Paradigms, Technologies, or a class of such platforms. The platform is listed along with how frequently the given weakness appears for that instance.

Languages

(Language-Independent classes): (Undetermined Prevalence)

+ Common Consequences

The table below specifies different individual consequences associated with the weakness. The Scope identifies the application security area that is violated, while the Impact describes the negative technical impact that arises if an adversary succeeds in exploiting this weakness. The Likelihood provides information about how likely the specific consequence is expected to be seen relative to the other consequences in the list. For example, there may be high likelihood that a weakness will be exploited to achieve a certain impact, but a low likelihood that it will be exploited to achieve a different impact.

ScopeImpactLikelihood
Other

Technical Impact: Varies by Context

+ Demonstrative Examples

Example 1

Both of these examples use a statistical PRNG seeded with the current value of the system clock to generate a random number:

(bad)
Example Language: Java 
Random random = new Random(System.currentTimeMillis());
int accountID = random.nextInt();
(bad)
Example Language:
srand(time());
int randNum = rand();

An attacker can easily predict the seed used by these PRNGs, and so also predict the stream of random numbers generated. Note these examples also exhibit CWE-338 (Use of Cryptographically Weak PRNG).

+ Potential Mitigations
Use non-predictable inputs for seed generation.

Phases: Architecture and Design; Requirements

Strategy: 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").

Phase: 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.
+ Memberships
This MemberOf Relationships table shows additional CWE Categories and Views that reference this weakness as a member. This information is often useful in understanding where a weakness fits within the context of external information sources.
+ Taxonomy Mappings
Mapped Taxonomy NameNode IDFitMapped Node Name
PLOVERPredictable Seed in PRNG
CERT Java Secure CodingMSC02-JGenerate strong random numbers
+ References
[REF-267] Information Technology Laboratory, National Institute of Standards and Technology. "SECURITY REQUIREMENTS FOR CRYPTOGRAPHIC MODULES". 2001-05-25. <http://csrc.nist.gov/publications/fips/fips140-2/fips1402.pdf>.
[REF-44] Michael Howard, David LeBlanc and John Viega. "24 Deadly Sins of Software Security". "Sin 20: Weak Random Numbers." Page 299. McGraw-Hill. 2010.
+ Content History
Submissions
Submission DateSubmitterOrganizationSource
PLOVER
Modifications
Modification DateModifierOrganizationSource
2008-07-01Sean EidemillerCigital
added/updated demonstrative examples
2008-07-01Eric DalciCigital
updated Time_of_Introduction
2008-09-08CWE Content TeamMITRE
updated Relationships, Taxonomy_Mappings
2009-03-10CWE Content TeamMITRE
updated Potential_Mitigations
2009-12-28CWE Content TeamMITRE
updated Potential_Mitigations
2010-06-21CWE Content TeamMITRE
updated Potential_Mitigations
2011-06-01CWE Content TeamMITRE
updated Common_Consequences, Relationships, Taxonomy_Mappings
2011-06-27CWE Content TeamMITRE
updated Common_Consequences
2011-09-13CWE Content TeamMITRE
updated Potential_Mitigations, References
2012-05-11CWE Content TeamMITRE
updated References, Relationships
2012-10-30CWE Content TeamMITRE
updated Demonstrative_Examples, Potential_Mitigations
2017-11-08CWE Content TeamMITRE
updated Applicable_Platforms, Demonstrative_Examples, Description, Modes_of_Introduction, Name, References, Relationships
Previous Entry Names
Change DatePrevious Entry Name
2017-11-08Predictable Seed in PRNG

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Page Last Updated: November 14, 2017