216
Relevant to the view "Software Development" (CWE-699)
Nature Type ID Name Page
MemberOf 137 Data Neutralization Issues 2311
Applicable Platforms
Language : Not Language-Specific (Prevalence = Undetermined)
Common Consequences
Scope Impact Likelihood
Confidentiality
Integrity
Availability
Execute Unauthorized Code or Commands
Read Application Data
Modify Application Data
Detection Methods
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
Potential Mitigations
Phase: Implementation
Strategy = Input Validation
Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use
a list of acceptable inputs that strictly conform to specifications. Reject any input that does not
strictly conform to specifications, or transform it into something that does. When performing
input validation, consider all potentially relevant properties, including length, type of input, the
full range of acceptable values, missing or extra inputs, syntax, consistency across related
fields, and conformance to business rules. As an example of business rule logic, "boat" may be
syntactically valid because it only contains alphanumeric characters, but it is not valid if the input
is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for
malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if
the code's environment changes. This can give attackers enough room to bypass the intended
validation. However, denylists can be useful for detecting potential attacks or determining which
inputs are so malformed that they should be rejected outright.
MemberOf Relationships
This MemberOf relationships table shows additional CWE Catgeories 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.
Nature Type ID Name Page
MemberOf 713 OWASP Top Ten 2007 Category A2 - Injection Flaws 629 2330
MemberOf 727 OWASP Top Ten 2004 Category A6 - Injection Flaws 711 2337
MemberOf 810 OWASP Top Ten 2010 Category A1 - Injection 809 2356
MemberOf 929 OWASP Top Ten 2013 Category A1 - Injection 928 2389
MemberOf 990 SFP Secondary Cluster: Tainted Input to Command 888 2413
MemberOf 1027 OWASP Top Ten 2017 Category A1 - Injection 1026 2435
MemberOf 1308 CISQ Quality Measures - Security 1305 2485
MemberOf 1340 CISQ Data Protection Measures 1340 2590
CWE Version 4.14
CWE-93: Improper Neutralization of CRLF Sequences ('CRLF Injection')
CWE-93: Improper Neutralization of CRLF Sequences ('CRLF Injection')