Modes Of Introduction
Implementation
Common Consequences
Scope |
Impact |
Likelihood |
Integrity | Unexpected State | |
Detection Methods
Fuzzing
Fuzz testing (fuzzing) is a powerful technique for generating large numbers of diverse inputs - either randomly or algorithmically - and dynamically invoking the code with those inputs. Even with random inputs, it is often capable of generating unexpected results such as crashes, memory corruption, or resource consumption. Fuzzing effectively produces repeatable test cases that clearly indicate bugs, which helps developers to diagnose the issues.
Effectiveness : High
Automated Static Analysis
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
Vulnerability Mapping Notes
Justification : This CWE entry is at the Base level of abstraction, which is a preferred level of abstraction for mapping to the root causes of vulnerabilities.
Comment : Carefully read both the name and description to ensure that this mapping is an appropriate fit. Do not try to 'force' a mapping to a lower-level Base/Variant simply to comply with this preferred level of abstraction.
Related Attack Patterns
CAPEC-ID |
Attack Pattern Name |
CAPEC-39 |
Manipulating Opaque Client-based Data Tokens In circumstances where an application holds important data client-side in tokens (cookies, URLs, data files, and so forth) that data can be manipulated. If client or server-side application components reinterpret that data as authentication tokens or data (such as store item pricing or wallet information) then even opaquely manipulating that data may bear fruit for an Attacker. In this pattern an attacker undermines the assumption that client side tokens have been adequately protected from tampering through use of encryption or obfuscation. |
Submission
Name |
Organization |
Date |
Date release |
Version |
PLOVER |
|
2006-07-19 +00:00 |
2006-07-19 +00:00 |
Draft 3 |
Modifications
Name |
Organization |
Date |
Comment |
Eric Dalci |
Cigital |
2008-07-01 +00:00 |
updated Time_of_Introduction |
CWE Content Team |
MITRE |
2008-09-08 +00:00 |
updated Relationships, Taxonomy_Mappings |
CWE Content Team |
MITRE |
2011-06-01 +00:00 |
updated Common_Consequences |
CWE Content Team |
MITRE |
2011-06-27 +00:00 |
updated Common_Consequences |
CWE Content Team |
MITRE |
2012-05-11 +00:00 |
updated Relationships |
CWE Content Team |
MITRE |
2013-07-17 +00:00 |
updated Description, Name, Type |
CWE Content Team |
MITRE |
2014-02-18 +00:00 |
updated Demonstrative_Examples |
CWE Content Team |
MITRE |
2014-07-30 +00:00 |
updated Relationships |
CWE Content Team |
MITRE |
2020-02-24 +00:00 |
updated Relationships |
CWE Content Team |
MITRE |
2021-03-15 +00:00 |
updated Demonstrative_Examples |
CWE Content Team |
MITRE |
2023-01-31 +00:00 |
updated Description |
CWE Content Team |
MITRE |
2023-04-27 +00:00 |
updated Detection_Factors, Relationships, Time_of_Introduction |
CWE Content Team |
MITRE |
2023-06-29 +00:00 |
updated Mapping_Notes |