CWE

Common Weakness Enumeration

A Community-Developed List of Software Weakness Types

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ID

CWE-342: Predictable Exact Value from Previous Values

Weakness ID: 342
Abstraction: Base
Structure: Simple
Status: Draft
Presentation Filter:
+ Description
An exact value or random number can be precisely predicted by observing previous values.
+ 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)
NatureTypeIDName
ChildOfClassClass330Use of Insufficiently Random Values
+ Relevant to the view "Development Concepts" (CWE-699)
NatureTypeIDName
ChildOfClassClass330Use of Insufficiently Random Values
+ 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
Implementation
+ 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

+ Observed Examples
ReferenceDescription
Firewall generates easily predictable initial sequence numbers (ISN), which allows remote attackers to spoof connections.
Listening TCP ports are sequentially allocated, allowing spoofing attacks.
Predictable TCP sequence numbers allow spoofing.
DNS resolver uses predictable IDs, allowing a local user to spoof DNS query results.
+ 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 [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.
NatureTypeIDName
MemberOfCategoryCategory905SFP Primary Cluster: Predictability
+ Taxonomy Mappings
Mapped Taxonomy NameNode IDFitMapped Node Name
PLOVERPredictable Exact Value from Previous Values
+ 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
2008-07-01Eric 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
2011-06-27CWE Content TeamMITRE
updated Common_Consequences
2011-09-13CWE Content TeamMITRE
updated Potential_Mitigations, References
2012-05-11CWE Content TeamMITRE
updated Observed_Examples, References, Relationships
2012-10-30CWE Content TeamMITRE
updated Potential_Mitigations
2017-11-08CWE Content TeamMITRE
updated Applicable_Platforms, References

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Page Last Updated: November 14, 2017