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ENV03-C. Sanitize the environment when invoking external programs
not call system().) Clear the environment and fill it with trusted or default values. This recommendation is a more specific instance of STR02-C. Sanitize data … (). In this compliant solution, the environment is cleared by clearenv(), and then the PATH and IFS variables are set to safe values before system() is invoked. SanitizingSTR02-C. Sanitize data passed to complex subsystems
. As a result, it is necessary to sanitize all string data passed to complex subsystems so that the resulting string is innocuous in the context in which it will be interpreted. These are some examples of complex subsystems: Command processor via a call to system() or similar function (also addressed in ENV03-C. SanitizeIDS33-PL. Sanitize untrusted data passed across a trust boundary
the subsystem must parse. Such data must be sanitized both because the subsystem may be unprepared to handle the malformed input and because unsanitized input may include an injection attack. In particular, programs must sanitize all string data that is passed to command interpreters or parsers so that the resultingIDS07-J. Sanitize untrusted data passed to the Runtime.exec() method
a crude form of component-based software engineering. Command and argument injection vulnerabilities occur when an application fails to sanitize untrusted input … , or when they start with a - or / to indicate a switch. Any string data that originates from outside the program's trust boundary must be sanitized before beingIDS08-J. Sanitize untrusted data included in a regular expression
be able to change the groupings by supplying untrusted input. Untrusted input should be sanitized before use to prevent regex injection. When the user must … and digits) before delivering the user-supplied string to the regex parser is a good input sanitization strategy. A programmer must provide only a very limited subsetInput Validation and Data Sanitization
. Sanitization: In many cases, the data is passed directly to a component in a different trusted domain. Data sanitization is the process of ensuring that data conforms to the requirements of the subsystem to which it is passed. Sanitization also involves ensuring that data conforms to security-related requirements regardingRule 00. Input Validation and Data Sanitization (IDS)
Rules Risk Assessment Summary Rule Severity Likelihood Remediation Cost Priority Level IDS00-J High Likely Medium P18 L1 IDS01-J High Probable Medium P12 L1 IDS03-J Medium Probable Medium P8 L2 IDS04-J Low Probable High P2 L3 IDS06-J Medium UnRule 01. Input Validation and Data Sanitization (IDS)
Information for Editors In order to have a new guideline automatically listed above be sure to label it ids https://confluence/label/seccode/ids and rule https://confluence/label/seccode/rule. Risk Assessment Summary Rule Severity Likelihood Remediation Cost Priority Level IDS30-PL High ProbRec. 01. Input Validation and Data Sanitization (IDS)
Information for Editors In order to have a new guideline automatically listed above be sure to label it ids https://confluence/label/seccode/ids and recommendation https://confluence/label/seccode/recommendation. Risk Assessment Summary Rule Severity Likelihood Remediation Cost Priority LevelRec. 00. Input Validation and Data Sanitization (IDS)
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