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ID

CWE-343: Predictable Value Range from Previous Values

Weakness ID: 343
Abstraction: Base
Status: Draft
Presentation Filter:
+ Description

Description Summary

The software'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.

Extended Description

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.

+ Time of Introduction
  • Architecture and Design
  • Implementation
+ Applicable Platforms

Languages

All

+ Common Consequences
ScopeEffect
Other

Technical Impact: Varies by context

+ Potential Mitigations

Increase the entropy used to seed a PRNG.

Phases: Architecture and Design; Requirements

Strategy: Libraries or Frameworks

Use products or modules that conform to FIPS 140-2 [R.343.1] 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.

+ Relationships
NatureTypeIDNameView(s) this relationship pertains toView(s)
ChildOfWeakness ClassWeakness Class330Use of Insufficiently Random Values
Development Concepts (primary)699
Research Concepts (primary)1000
ChildOfCategoryCategory905SFP Primary Cluster: Predictability
Software Fault Pattern (SFP) Clusters (primary)888
+ Taxonomy Mappings
Mapped Taxonomy NameNode IDFitMapped Node Name
PLOVERPredictable Value Range from Previous Values
+ References
[R.343.1] [REF-1] 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>.
[R.343.2] Michal Zalewski. "Strange Attractors and TCP/IP Sequence Number Analysis". 2001. <http://www.bindview.com/Services/Razor/Papers/2001/tcpseq.cfm>.
[R.343.3] [REF-17] 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
PLOVERExternally Mined
Modifications
Modification DateModifierOrganizationSource
2008-07-01Eric DalciCigitalExternal
updated Time_of_Introduction
2008-09-08CWE Content TeamMITREInternal
updated Relationships, Taxonomy_Mappings
2008-10-14CWE Content TeamMITREInternal
updated Description
2009-03-10CWE Content TeamMITREInternal
updated Potential_Mitigations
2009-12-28CWE Content TeamMITREInternal
updated Potential_Mitigations
2010-06-21CWE Content TeamMITREInternal
updated Potential_Mitigations
2011-06-01CWE Content TeamMITREInternal
updated Common_Consequences
2011-06-27CWE Content TeamMITREInternal
updated Common_Consequences
2011-09-13CWE Content TeamMITREInternal
updated Potential_Mitigations, References
2012-05-11CWE Content TeamMITREInternal
updated References, Relationships
2012-10-30CWE Content TeamMITREInternal
updated Potential_Mitigations

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Page Last Updated: May 05, 2017