Taint analysis

Semgrep supports taint analysis (or taint tracking) through taint rules (specified by adding mode: taint to your rule). Taint analysis is a data-flow analysis that tracks the flow of untrusted, or tainted data throughout the body of a function or method. Tainted data originate from tainted sources. If tainted data is not transformed or checked accordingly (sanitized), taint analysis reports a finding whenever tainted data reach a vulnerable function, called a sink. Tainted data flow from sources to sinks through propagators, such as assignments, or function calls.

The following video provides a quick overview of taint mode:

Getting started

Taint tracking rules must specify mode: taint, which enables the following operators:

pattern-sources (required)
pattern-propagators (optional)
pattern-sanitizers (optional)
pattern-sinks (required)

These operators (which act as pattern-either operators) take a list of patterns that specify what is considered a source, a propagator, a sanitizer, or a sink. Note that you can use any pattern operator and you have the same expressive power as in a mode: search rule.

For example:

Here Semgrep tracks the data returned by get_user_input(), which is the source of taint. Think of Semgrep running the pattern get_user_input(...) on your code, finding all places where get_user_input gets called, and labeling them as tainted. That is exactly what is happening under the hood!

The rule specifies the sanitizer sanitize_input(...), so any expression that matches that pattern is considered sanitized. In particular, the expression sanitize_input(data) is labeled as sanitized. Even if data is tainted, as it occurs inside a piece of sanitized code, it does not produce any findings.

Finally, the rule specifies that anything matching either html_output(...) or eval(...) should be regarded as a sink. There are two calls html_output(data) that are both labeled as sinks. The first one in route1 is not reported because data is sanitized before reaching the sink, whereas the second one in route2 is reported because the data that reaches the sink is still tainted.

You can find more examples of taint rules in the Semgrep Registry, for instance: express-sandbox-code-injection.

info

Metavariables used in pattern-sources are considered different from those used in pattern-sinks, even if they have the same name! See Metavariables, rule message, and unification for further details.

Sources

A taint source is specified by a pattern. Like in a search-mode rule, you can start this pattern with one of the following keys: pattern, patterns, pattern-either, pattern-regex. Note that any subexpression that is matched by this pattern will be regarded as a source of taint.

In addition, taint sources accept the following options:

Option	Type	Default	Description
`exact`	true	`false`	See Exact sources.
`by-side-effect`	only	`false`	See Sources by side-effect.
`control` (Pro) 🧪	true	`false`	See Control sources.

Example:

pattern-sources:
- pattern: source(...)

Exact sources

Given the source specification below, and a piece of code such as source(sink(x)), the call sink(x) is reported as a tainted sink.

pattern-sources:
- pattern: source(...)

The reason is that the pattern source(...) matches all of source(sink(x)), and that makes Semgrep consider every subexpression in that piece of code as being a source. In particular, x is a source, and it is being passed into sink!

This is the default for historical reasons, but it may change in the future.

It is possible to instruct Semgrep to only consider as taint sources the "exact" matches of a source pattern by setting exact: true:

pattern-sources:
- pattern: source(...)
  exact: true

Once the source is "exact," Semgrep will no longer consider subexpressions as taint sources, and sink(x) inside source(sink(x)) will not be reported as a tainted sink (unless x is tainted in some other way).

For many rules this distinction is not very meaningful because it does not always make sense that a sink occurs inside the arguments of a source function.

note

If one of your rules relies on non-exact matching of sources, we advice you to make it explicit with exact: false, even if it is the current default, so that your rule does not break if the default changes.

Sources by side-effect

Consider the following hypothetical Python code, where make_tainted is a function that makes its argument tainted by side-effect:

make_tainted(my_set)
sink(my_set)

This kind of source can be specified by setting by-side-effect: true:

pattern-sources:
  - patterns:
      - pattern: make_tainted($X)
      - focus-metavariable: $X
    by-side-effect: true

When this option is enabled, and the source specification matches a variable (or in general, an l-value) exactly, then Semgrep assumes that the variable (or l-value) becomes tainted by side-effect at the precise places where the source specification produces a match.

The matched occurrences themselves are considered tainted; that is, the occurrence of x in make_tainted(x) is itself tainted too. If you do not want this to be the case, then set by-side-effect: only instead.

note

You must use focus-metavariable: $X to focus the match on the l-value that you want to taint, otherwise by-side-effect does not work.

If the source does not set by-side-effect, then only the very occurrence of x in make_tainted(x) will be tainted, but not the occurrence of x in sink(x). The source specification matches only the first occurrence and, without by-side-effect: true, Semgrep does not know that make_tainted is updating the variable x by side-effect. Thus, a taint rule using such a specification does not produce any finding.

info

You could be tempted to write a source specification as the following example (and this was the official workaround before by-side-effect):

pattern-sources:
- patterns:
  - pattern-inside: |
      make_tainted($X)
      ...
  - pattern: $X

This tells Semgrep that every occurrence of $X after make_tainted($X) must be considered a source.

This approach has two main limitations. First, it overrides any sanitization that can be performed on the code matched by $X. In the example code below, the call sink(x) is reported as tainted despite x having been sanitized!

make_tainted(x)
x = sanitize(x)
sink(x) # false positive

Note also that ... ellipses operator has limitations. For example, in the code below Semgrep does not match any finding if such source specification is in use:

if cond:
    make_tainted(x)
sink(x) # false negative

The by-side-effect option was added precisely to address those limitations. However, that kind of workaround can still be useful in other situations!

Function arguments as sources

To specify that an argument of a function must be considered a taint source, simply write a pattern that matches that argument:

pattern-sources:
  - patterns:
    - pattern-inside: |
        def foo($X, ...):
          ...
    - focus-metavariable: $X

Note that the use of focus-metavariable: $X is very important, and using pattern: $X is not equivalent. With focus-metavariable: $X, Semgrep matches the formal parameter exactly. Click "Open in Playground" below and use "Inspect Rule" to visualize what the source is matching.

The following example does the same with this other taint rule that uses pattern: $X. The pattern: $X does not match the formal parameter itself, but matches all its uses inside the function definition. Even if x is sanitized via x = sanitize(x), the occurrence of x inside sink(x) is a taint source itself (due to pattern: $X) and so sink(x) is tainted!

Control sources (Pro) 🧪

Control taint sources is a Semgrep Pro feature.

Typically taint analysis tracks the flow of tainted data, but taint sources can also track the flow of tainted control by setting control: true.

pattern-sources:
- pattern: source(...)
  control: true

This is useful for checking reachability, that is to check if from a given code location the control-flow can reach another code location, regardless of whether there is any flow of data between them. In the following example we check whether foo() could be followed by bar():

By using a control source, you can define a context from which Semgrep detects if a call to some other code, such as a sink, can be reached.

note

Use taint labels to combine both data and control sources in the same rule.

Sanitizers

A taint sanitizer is specified by a pattern. Like in a search-mode rule, you can start this pattern with one of the following keys: pattern, patterns, pattern-either, pattern-regex. Note that any subexpression that is matched by this pattern will be regarded as sanitized.

In addition, taint sanitizers accept the following options:

Option	Type	Default	Description
`exact`	true	`false`	See Exact sanitizers.
`by-side-effect`	only	`false`	See Sanitizers by side-effect.

Example:

pattern-sanitizers:
- pattern: sanitize(...)

Exact sanitizers

Given the sanitizer specification below, and a piece of code such as sanitize(sink("taint")), the call sink("taint") is not reported.

pattern-sanitizers:
- pattern: sanitize(...)

The reason is that the pattern sanitize(...) matches all of sanitize(sink("taint")), and that makes Semgrep consider every subexpression in that piece of code as being sanitized. In particular, "taint" is considered to be sanitized!

This is the default for historical reasons, but it may change in the future.

It is possible to instruct Semgrep to only consider as sanitized the "exact" matches of a sanitizer pattern by setting exact: true:

pattern-sanitizers:
- pattern: sanitize(...)
  exact: true

Once the source is "exact," Semgrep will no longer consider subexpressions as sanitized, and sink("taint") inside sanitize(sink("taint")) will be reported as a tainted sink.

For many rules this distinction is not very meaningful because it does not always make sense that a sink occurs inside the arguments of a sanitizer function.

note

If one of your rules relies on non-exact matching of sanitizers, We at Semgrep advise you to make it explicit with exact: false, even if it is the current default, so that your rule does not break if the default changes.

Sanitizers by side-effect

Consider the following hypothetical Python code, where it is guaranteed that after check_if_safe(x), the value of x must be a safe one.

x = source()
check_if_safe(x)
sink(x)

This kind of sanitizer can be specified by setting by-side-effect: true:

pattern-sanitizers:
  - patterns:
      - pattern: check_if_safe($X)
      - focus-metavariable: $X
    by-side-effect: true

When this option is enabled, and the sanitizer specification matches a variable (or in general, an l-value) exactly, then Semgrep assumes that the variable (or l-value) is sanitized by side-effect at the precise places where the sanitizer specification produces a match.

note

It is important to use focus-metavariable: $X to focus the match on the l-value that we want to sanitize, otherwise by-side-effect does not work as expected.

If the sanitizer does not set by-side-efect, then only the very occurrence of x in check_if_safe(x) will be sanitized, but not the occurrence of x in sink(x). The sanitizer specification matches only the first occurrence and, without by-side-effect: true, Semgrep does not know that check_if_safe is updating/sanitizing the variable x by side-effect. Thus, a taint rule using such specification does produce a finding for sink(x) in the example above.

info

You can be tempted to write a sanitizer specification as the one below (and this was the official workaround before by-side-effect):

pattern-sanitizers:
- patterns:
  - pattern-inside: |
      check_if_safe($X)
      ...
  - pattern: $X

This tells Semgrep that every occurrence of $X after check_if_safe($X) must be considered sanitized.

This approach has two main limitations. First, it overrides any further tainting that can be performed on the code matched by $X. In the example code below, the call sink(x) is not reported as tainted despite x having been tainted!

check_if_safe(x)
x = source()
sink(x) # false negative

Note also that ... ellipses operator has limitations. For example, in the code below Semgrep still matches despite x having been sanitized in both branches:

if cond:
    check_if_safe(x)
else
    check_if_safe(x)
sink(x) # false positive

The by-side-effect option was added precisely to address those limitations. However, that kind of workaround can still be useful in other situations!

Sinks

A taint sink is specified by a pattern. Like in a search-mode rule, you can start this pattern with one of the following keys: pattern, patterns, pattern-either, pattern-regex. Unlike sources and sanitizers, by default Semgrep does not consider the subexpressions of the matched expressions as sinks.

In addition, taint sinks accept the following options:

Option	Type	Default	Description
`exact`	true	`true`	See Non-exact sinks.
`at-exit` (Pro) 🧪	true	`false`	See At-exit sinks.

Example:

pattern-sinks:
- pattern: sink(...)

Non-exact sinks

Given the sink specification below, a piece of code such as sink("foo" if tainted else "bar") will not be reported as a tainted sink.

pattern-sources:
- pattern: sink(...)

This is because Semgrep considers that the sink is the argument of the sink function, and the actual argument being passed is "foo" if tainted else "bar" that evaluates to either "foo" or "bar", and neither of them are tainted.

It is possible to instruct Semgrep to consider as a taint sink any of the subexpressions matching the sink pattern, by setting exact: false:

pattern-sinks:
- pattern: sink(...)
  exact: false

Once the sink is "non-exact" Semgrep will consider subexpressions as taint sinks, and tainted inside sink("foo" if tainted else "bar") will then be reported as a tainted sink.

Function arguments as sinks

We can specify that only one (or a subset) of the arguments of a function is the actual sink by using focus-metavariable:

pattern-sinks:
  - patterns:
    - pattern: sink($SINK, ...)
    - focus-metavariable: $SINK

This rule causes Semgrep to only annotate the first parameter passed to sink as the sink, rather than the function sink itself. If taint goes into any other parameter of sink, then that is not considered a problem.

Anything that you can match with Semgrep can be made into a sink, like the index in an array access:

pattern-sinks:
  - patterns:
    - pattern-inside: $ARRAY[$SINK]
    - focus-metavariable: $SINK

note

If you specify a sink such as sink(...) then any tainted data passed to sink, through any of its arguments, results in a finding.

At-exit sinks (Pro) 🧪

At-exit taint sinks is a Semgrep Pro feature.

At-exit sinks are meant to facilitate writing leak-detection rules using taint mode. By setting at-exit: true you can restrict a sink specification to only match at "exit" statements, that is statements after which the control-flow will exit the function being analyzed.

pattern-sinks:
- pattern-either:
  - pattern: return ...
  - pattern: $F(...)
  at-exit: true

The above sink pattern matches either return statements (which are always "exit" statements), or function calls occurring as "exit" statements.

Unlike regular sinks, at-exit sinks trigger a finding if any tainted l-value reaches the location of the sink. For example, the at-exit sink specification above will trigger a finding at a return 0 statement if some tainted l-value reaches the return, even if return 0 itself is not tainted. The location itself is the sink rather than the code that is at that location.

You can use this, for example, to check that file descriptors are being closed within the same function where they were opened.

The print(content) statement is reported because the control flow exits the function at that point, and the file has not been closed.

Propagators (Pro)

Custom taint propagators is a Semgrep Pro feature.

By default, tainted data automatically propagates through assignments, operators, and function calls (from inputs to output). However, there are other ways in which taint can propagate, which can require language or library-specific knowledge that Semgrep does not have built-in.

A taint propagator requires a pattern to be specified. Like in a search-mode rule, you can start this pattern with one of the following keys: pattern, patterns, pattern-either, pattern-regex.

A propagator also needs to specify the origin (from) and the destination (to) of the taint to be propagated.

Field	Type	Description
`from`	metavariable	Source of propagation.
`to`	metavariable	Destination of propagation.

In addition, taint propagators accept the following options:

Option	Type	Default	Description
`by-side-effect`	true	`true`	See Propagation without side-effect.

For example, given the following propagator, if taint goes into the second argument of strcpy, its first argument will get the same taint:

pattern-propagators:
- pattern: strcpy($DST, $SRC)
  from: $SRC
  to: $DST

info

Taint propagators only work intra-procedurally, that is, within a function or method. You cannot use taint propagators to propagate taint across different functions/methods. Use inter-procedural analysis.

Understanding custom propagators

Consider the following Python code where an unsafe user_input is stored into a set data structure. A random element from set is then passed into a sink function. This random element can be user_input itself, leading to an injection vulnerability!

def test(s):
    x = user_input
    s = set([])
    s.add(x)
    #ruleid: test
    sink(s.pop())

The following rule cannot find the above-described issue. The reason is that Semgrep is not aware that executing s.add(x) makes x one of the elements in the set data structure s.

mode: taint
pattern-sources:
- pattern: user_input
pattern-sinks:
- pattern: sink(...)

The use of taint propagators enables Semgrep to propagate taint in this and other scenarios. Taint propagators are specified under the pattern-propagators key:

pattern-propagators:
- pattern: $S.add($E)
  from: $E
  to: $S

In the example above, Semgrep finds the pattern $S.add($E), and it checks whether the code matched by $E is tainted. If it is tainted, Semgrep propagates that same taint to the code matched by $S. Thus, adding tainted data to a set marks the set itself as tainted.

Note that s becomes tainted by side-effect after s.add(x), this is due to by-side-effect: true being the default for propagators, and because s is an l-value.

In general, a taint propagator must specify:

A pattern containing two metavariables. These two metavariables specify where taint is propagated from and to.
The to and from metavariables. These metavariables should match an expression.
- The from metavariable specifies the entry point of the taint.
- The to metavariable specifies where the tainted data is propagated to, typically an object or data structure. If option by-side-effect is enabled (as it is by default) and the to metavariable matches an l-value, the propagation is side-effectful.

In the example above, pattern $S.add($E) includes two metavariables $S and $E. Given from: $E and to: $S, and with $E matching x and $S matching s, when x is tainted then s becomes tainted (by side-effect) with the same taint as x.

Another situation where taint propagators can be useful is to specify in Java that, when iterating a collection that is tainted, the individual elements must also be considered tainted:

pattern-propagators:
- pattern: $C.forEach(($X) -> ...)
  from: $C
  to: $X

Propagation without side-effect

Taint propagators can be used in very imaginative ways, and in some cases you may not want taint to propagate by side-effect. This can be achieved by disabling by-side-effect, which is enabled by default.

For example:

pattern-propagators:
  - patterns:
    - pattern: |
        if something($FROM):
          ...
          $TO()
          ...
    from: $FROM
    to: $TO
    by-side-effect: false

The propagator above specifies that inside an if block, where the condition is something($FROM), we want to propagate taint from $FROM to any function that is being called without arguments, $TO().

Because the rule disables by-side-effect, the sink occurrence that is inside the if block is tainted, but this does not affect the sink occurrence outside the if block.

Findings

Taint findings are accompanied by a taint trace that explains how the taint flows from source to sink.

Deduplication of findings

Semgrep tracks all the possible ways that taint can reach a sink, but at present it only reports one taint trace among the possible ones. Click "Open in Playground" in the example below, run the example to get one finding, and then ask the Playground to visualize the dataflow of the finding. Even though sink can be tainted via x or via y, the trace will only show you one of these possibilities. If you replace x = user_input with x = "safe", then Semgrep will then report the taint trace via y.

Report findings on the sources (Pro)

Reporting findings on the source of taint is a Semgrep Pro feature.

By default Semgrep reports taint findings at the location of the sink being matched. You must look at the taint trace to identify where the taint is coming from. It is also possible to make Semgrep report the findings at the location of the taint sources, by setting the rule-level option taint_focus_on to source. Then

options:
  taint_focus_on: source

The deduplication of findings still applies in this case. While Semgrep will now report all the taint sources, if a taint source can reach multiple sinks, the taint trace will only inform you about one of them.

Minimizing false positives

The following rule options can be used to minimize false positives:

Rule option	Default	Description
`taint_assume_safe_booleans`	`false`	Boolean data is never considered tainted (works better with type annotations).
`taint_assume_safe_numbers`	`false`	Numbers (integers, floats) are never considered tainted (works better with type annotations).
`taint_assume_safe_indexes`	`false`	An index expression `I` tainted does not make an access expression `E[I]` tainted (it is only tainted if `E` is tainted).
`taint_assume_safe_functions`	`false`	A function call like `F(E)` is not considered tainted even if `E` is tainted. (When using Pro's inter-procedural taint analysis, this only applies to functions for which Semgrep cannot find a definition.)
`taint_only_propagate_through_assignments` 🧪	`false`	Disables all implicit taint propagation except for assignments.

Restrict taint by type (Pro)

By enabling taint_assume_safe_booleans Semgrep automatically sanitizes Boolean expressions when it can infer that the expression resolves to Boolean.

For example, comparing a tainted string against a constant string will not be considered a tainted expression:

Similarly, enabling taint_assume_safe_numbers Semgrep will automatically sanitize numeric expressions when it can infer that the expression is numeric.

You could define explicit sanitizers that clean the taint from Boolean or numeric expressions, but these options are more convenient and also more efficient.

note

Semgrep Pro's ability to infer types for expressions varies depending on the language. For example, in Python type annotations are not always present, and the + operator can also be used to concatenate strings. Semgrep also ignores the types of functions and classes coming from third-party libraries.

Assume tainted indexes are safe

By default, Semgrep assumes that accessing an array-like object with a tainted index (that is, obj[tainted]) is itself a tainted expression, even if the object itself is not tainted. Setting taint_assume_safe_indexes: true makes Semgrep assume that these expressions are safe.

Assume function calls are safe

note

We refer to a function call as opaque when Semgrep does not have access to its definition, to examine it and determine its "taint behavior" (for example, whether the function call propagates or not any taint that comes through its inputs). In Semgrep Community Edition (CE), where taint analysis is intra-procedural, all function calls are opaque. In Semgrep Pro, with inter-procedural taint analysis, an opaque function could be one coming from a third-party library.

By default Semgrep considers that an opaque function call propagates any taint passed through any of its arguments to its output.

For example, in the code below, some_safe_function receives tainted data as input, so Semgrep assumes that it also returns tainted data as output. As a result, a finding is produced.

var x = some_safe_function(tainted);
sink(x); // undesired finding here

This can generate false positives, and for certain rules on certain codebases it can produce a high amount of noise.

Setting taint_assume_safe_functions: true makes Semgrep assume that opaque function calls are safe and do not propagate any taint. If it is desired that specific functions do propagate taint, then that can be achieved via custom propagators:

Propagate only through assignments 🧪

Setting taint_only_propagate_through_assignments: true makes Semgrep to only propagate taint through trivial assignments of the form <l-value> = <tainted-expression>. It requires the user to be explicit about any other kind of taint propagation that is to be performed.

For example, neither unsafe_function(tainted) nor tainted_string + "foo" will be considered tainted expressions:

Metavariables, rule message, and unification

The patterns specified by pattern-sources and pattern-sinks (and pattern-sanitizers) are all independent of each other. If a metavariable used in pattern-sources has the same name as a metavariable used in pattern-sinks, these are still different metavariables.

In the message of a taint-mode rule, you can refer to any metavariable bound by pattern-sinks, as well as to any metavariable bound by pattern-sources that does not conflict with a metavariable bound by pattern-sinks.

Semgrep can also treat metavariables with the same name as the same metavariable, simply set taint_unify_mvars: true using rule options. Unification enforces that whatever a metavariable binds to in each of these operators is, syntactically speaking, the same piece of code. For example, if a metavariable binds to a code variable x in the source match, it must bind to the same code variable x in the sink match. In general, unless you know what you are doing, avoid metavariable unification between sources and sinks.

The following example demonstrates the use of source and sink metavariable unification:

Inter-procedural analysis (Pro)

Inter-procedural taint analysis is a Semgrep Pro feature.

Semgrep can perform inter-procedural taint analysis, that is, to track taint across multiple functions.

In the example below, user_input is passed to foo as input and, from there, flows to the sink at line 3, through a call chain involving three functions. Semgrep is able to track this and report the sink as tainted. Semgrep also provides an inter-procedural taint trace that explains how exactly user_input reaches the sink(z) statement (click "Open in Playground" then click "dataflow" in the "Matches" panel).

Using the CLI option --pro-intrafile, Semgrep will perform inter-procedural (across functions) intra-file (within one file) analysis. That is, it will track taint across functions, but it will not cross file boundaries. This is supported for essentially every language, and performance is very close to that of intra-procedural taint analysis.

Using the CLI option --pro, Semgrep will perform inter-procedural (across functions) as well as inter-file (across files) analysis. Inter-file analysis is only supported for a subset of languages. For a rule to run inter-file it also needs to set interfile: true:

options:
  interfile: true

Memory requirements for inter-file analysis: While interfile analysis is more powerful, it also demands more memory resources. The Semgrep team advises a minimum of 4 GB of memory per core, but recommend 8 GB per core or more. The amount of memory needed depends on the codebase and on the number of interfile rules being run.

Taint mode sensitivity

Field sensitivity

The taint engine provides basic field sensitivity support. It can:

Track that x.a.b is tainted, but x or x.a is not tainted. If x.a.b is tainted, any extension of x.a.b (such as x.a.b.c) is considered tainted by default.
Track that x.a is tainted, but remember that x.a.b has been sanitized. Thus the engine records that x.a.b is not tainted, but x.a or x.a.c are still tainted.

note

The taint engine does track taint per variable and not per object in memory. The taint engine does not track aliasing at present.

Index sensitivity (Pro)

Index sensitivity is a Semgrep Pro feature.

Semgrep Pro has basic index sensitivity support:

Only for accesses using the built-in a[E] syntax.
Works for statically constant indexes that may be either integers (e.g. a[42]) or strings (e.g. a["foo"]).
If an arbitrary index a[i] is sanitized, then every index becomes clean of taint.

Taint labels (Pro) 🧪

Taint labels increase the expressiveness of taint analysis by allowing you to specify and track different kinds of tainted data in one rule using labels. This functionality has various uses, for example, when data becomes dangerous in several steps that are hard to specify through single pair of source and sink.

To include taint labels into a taint mode rule, follow these steps:

Attach a label key to the taint source. For example, label: TAINTED or label: INPUT. See the example below:
```
  pattern-sources:
    - pattern: user_input
      label: INPUT
```
Semgrep accepts any valid Python identifier as a label.
Restrict a taint source to a subset of labels using the requires key. Extending the previous example, see the requires: INPUT below:
```
    pattern-sources:
      - pattern: user_input
        label: INPUT
      - pattern: evil(...)
        requires: INPUT
        label: EVIL
```
Combine labels using the requires key. To combine labels, use Python Boolean operators. For example: requires: LABEL1 and not LABEL2.

Use the requires key to restrict a taint sink in the same way as source:

    pattern-sinks:
      - pattern: sink(...)
        requires: EVIL

info

Semgrep accepts valid Python identifiers as labels.
Restrict a source to a subset of labels using the requires key. You can combine more labels in the requires key using Python Boolean operators. For example: requires: LABEL1 and not LABEL2.
Restrict a sink also. The extra taint is only produced if the source itself is tainted and satisfies the requires formula.

In the example below, let's say that user_input is dangerous but only when it passes through the evil function. This can be specified with taint labels as follows:

Not finding what you need in this doc? Ask questions in our Community Slack group, or see Support for other ways to get help.

Getting started​

Sources​

Exact sources​

Sources by side-effect​

Function arguments as sources​

Control sources (Pro) 🧪​

Sanitizers​

Exact sanitizers​

Sanitizers by side-effect​

Sinks​

Non-exact sinks​

Function arguments as sinks​

At-exit sinks (Pro) 🧪​

Propagators (Pro)​

Understanding custom propagators​

Propagation without side-effect​

Findings​

Deduplication of findings​

Report findings on the sources (Pro)​

Minimizing false positives​

Restrict taint by type (Pro)​

Assume tainted indexes are safe​

Assume function calls are safe​

Propagate only through assignments 🧪​

Metavariables, rule message, and unification​

Inter-procedural analysis (Pro)​

Taint mode sensitivity​

Field sensitivity​

Index sensitivity (Pro)​

Taint labels (Pro) 🧪​

Getting started

Sources

Exact sources

Sources by side-effect

Function arguments as sources

Control sources (Pro) 🧪

Sanitizers

Exact sanitizers

Sanitizers by side-effect

Sinks

Non-exact sinks

Function arguments as sinks

At-exit sinks (Pro) 🧪

Propagators (Pro)

Understanding custom propagators

Propagation without side-effect

Findings

Deduplication of findings

Report findings on the sources (Pro)

Minimizing false positives

Restrict taint by type (Pro)

Assume tainted indexes are safe

Assume function calls are safe

Propagate only through assignments 🧪

Metavariables, rule message, and unification

Inter-procedural analysis (Pro)

Taint mode sensitivity

Field sensitivity

Index sensitivity (Pro)

Taint labels (Pro) 🧪