# Analyzing data flow in JavaScript and TypeScript¶

## Overview¶

This topic describes how data flow analysis is implemented in the QL libraries for JavaScript/TypeScript and includes examples to help you write your own data flow queries. The following sections describe how to utilize the QL libraries for local data flow, global data flow, and taint tracking.

As our running example, we will develop a query that identifies command-line arguments that are passed as a file path to the standard Node.js readFile function. While this is not a problematic pattern as such, it is typical of the kind of reasoning that is frequently used in security queries.

For a more general introduction to modeling data flow in QL, see Introduction to data flow analysis in QL.

## Data flow nodes¶

Both local and global data flow, as well as taint tracking, work on a representation of the program known as the data flow graph. Nodes on the data flow flow graph may also correspond to nodes on the abstract syntax tree, but they are not the same. While AST nodes belong to class ASTNode and its subclasses, data flow nodes belong to class DataFlow::Node and its subclasses:

• DataFlow::ValueNode: a value node, that is, a data flow node that corresponds either to an expression, or to a declaration of a function, class, TypeScript namespace, or TypeScript enum.
• DataFlow::SsaDefinitionNode: a data flow node that corresponds to an SSA variable, that is, a local variable with additional information to reason more precisely about different assignments to the same variable. This kind of data flow node does not correspond to an AST node.
• DataFlow::PropRef: a data flow node that corresponds to a read or a write of an object property, for example, in an assignment, in an object literal, or in a destructuring assignment.
• DataFlow::PropRead, DataFlow::PropWrite: subclasses of DataFlow::PropRef that correspond to reads and writes, respectively.

Apart from these fairly general classes, there are some more specialized classes:

• DataFlow::ParameterNode: a data flow node that corresponds to a function parameter.
• DataFlow::InvokeNode: a data flow node that corresponds to a function call; its subclasses DataFlow::NewNode and DataFlow::CallNode represent calls with and without new respectively, while DataFlow::MethodCallNode represents method calls. Note that these classes also model reflective calls using .call and .apply, which do not correspond to any AST nodes.
• DataFlow::ThisNode: a data flow node that corresponds to the value of this in a function or top level. This kind of data flow node also does not correspond to an AST node.
• DataFlow::GlobalVarRefNode: a data flow node that corresponds to a direct reference to a global variable. This class is rarely used directly, instead you would normally use the predicate globalVarRef (introduced below), which also considers indirect references through window or global this.
• DataFlow::FunctionNode, DataFlow::ObjectLiteralNode, DataFlow::ArrayLiteralNode: a data flow node that corresponds to a function (expression or declaration), an object literal, or an array literal, respectively.
• DataFlow::ClassNode: a data flow node corresponding to a class, either defined using an ECMAScript 2015 class declaration or an old-style constructor function.
• DataFlow::ModuleImportNode: a data flow node corresponding to an ECMAScript 2015 import or an AMD or CommonJS require import.

The following predicates are available for mapping from AST nodes and other elements to their corresponding data flow nodes:

• DataFlow::valueNode(x): maps x, which must be an expression or a declaration of a function, class, namespace or enum, to its corresponding DataFlow::ValueNode.
• DataFlow::ssaDefinitionNode(ssa): maps an SSA definition ssa to its corresponding DataFlow::SsaDefinitionNode.
• DataFlow::parameterNode(p): maps a function parameter p to its corresponding DataFlow::ParameterNode.
• DataFlow::thisNode(s): maps a function or top-level s to the DataFlow::ThisNode representing the value of this in s.

Class DataFlow::Node also has a member predicate asExpr() that you can use to map from a DataFlow::ValueNode to the expression it corresponds to. Note that this predicate is undefined for other kinds of nodes, and for value nodes that do not correspond to expressions.

There are also some other predicates available for accessing commonly used data flow nodes:

• DataFlow::globalVarRef(g): gets a data flow node corresponding to an access to global variable g, either directly or through window or (top-level) this. For example, you can use DataFlow::globalVarRef("document") to find references to the DOM document object.
• DataFlow::moduleMember(p, m): gets a data flow node that references a member m of a module loaded from path p. For example, you can use DataFlow::moduleMember("fs", "readFile") to find references to the fs.readFile function from the Node.js standard library.

## Local data flow¶

Local data flow is data flow within a single function. Data flow through function calls and returns or through property writes and reads is not modeled.

Local data flow is faster to compute and easier to use than global data flow, but less complete. It is, however, sufficient for many purposes.

To reason about local data flow, use the member predicates getAPredecessor and getASuccessor on DataFlow::Node. For a data flow node nd, nd.getAPredecessor() returns all data flow nodes from which data flows to nd in one local step. Conversely, nd.getASuccessor() returns all nodes to which data flows from nd in one local step.

To follow one or more steps of local data flow, use the transitive closure operator +, and for zero or more steps the reflexive transitive closure operator *.

For example, the following query finds all data flow nodes source whose value may flow into the first argument of a call to a method with name readFile:

import javascript

where
select source


### Source nodes¶

Explicit reasoning about data flow edges can be cumbersome and is rare in practice. Typically, we are not interested in flow originating from arbitrary nodes, but from nodes that in some sense are the “source” of some kind of data, either because they create a new object, such as object literals or functions, or because they represent a point where data enters the local data flow graph, such as parameters or property reads.

The data flow library represents such nodes by the class DataFlow::SourceNode, which provides a convenient API to reason about local data flow involving source nodes.

By default, the following kinds of data flow nodes are considered source nodes:

• classes, functions, object and array literals, regular expressions, and JSX elements
• property reads, global variable references and this nodes
• function parameters
• function calls
• imports

You can extend the set of source nodes by defining additional subclasses of DataFlow::SourceNode::Range.

The DataFlow::SourceNode class defines a number of member predicates that can be used to track where data originating from a source node flows, and to find places where properties are accessed or methods are called on them.

For example, the following query finds all references to properties of process.argv, the array through which Node.js applications receive their command-line arguments:

import javascript



First, we use DataFlow::globalVarRef (mentioned above) to find all references to the global variable process. Since global variable references are source nodes, we can then use the predicate getAPropertyRead (defined in class DataFlow::SourceNode) to find all places where the property argv of that global variable is read. The results of this predicate are again source nodes, so we can chain it with a call to getAPropertyReference, which is a predicate that finds all references to any property (even references with a computed name) on its base source node.

Note that many predicates on DataFlow::SourceNode have source nodes as their result in turn, allowing calls to be chained to concisely express the relationship between several data flow nodes.

Most importantly, predicates like getAPropertyRead implicitly follow local data flow, so the above query not only finds direct property references like process.argv[2], but also more indirect ones as in this example:

var args = process.argv;
var firstArg = args[2];


Analogous to getAPropertyRead there is also a predicate getAPropertyWrite for identifying property writes.

Another common task is to find calls to a function originating from a source node. For this purpose, DataFlow::SourceNode offers predicates getACall, getAnInstantiation and getAnInvocation: the first one only considers invocations without new, the second one only invocations with new, and the third one considers all invocations.

We can use these predicates in combination with DataFlow::moduleMember (mentioned above) to find calls to the function readFile imported from the standard Node.js fs library:

import javascript



For identifying method calls there is also a predicate getAMethodCall, and the slightly more general getAMemberCall. The difference between the two is that the former only finds calls that have the syntactic shape of a method call such as x.m(...), while the latter also finds calls where x.m is first stored into a local variable f and then invoked as f(...).

Finally, the predicate flowsTo(nd) holds for any node nd into which data originating from the source node may flow. Conversely, DataFlow::Node offers a predicate getALocalSource() that can be used to find any source node that flows to it.

Putting all of the above together, here is a query that finds (local) data flow from command line arguments to readFile calls:

import javascript

from DataFlow::SourceNode arg, DataFlow::CallNode call
where
arg.flowsTo(call.getArgument(0))
select arg, call


There are two points worth making about the source node API:

1. All data flow tracking is purely local, and in particular flow through global variables is not tracked. If args in our process.argv example above is a global variable, then the query will not find the reference through args[2].
2. Strings are not source nodes and cannot be tracked using this API. You can, however, use the mayHaveStringValue predicate on class DataFlow::Node to reason about the possible string values flowing into a data flow node.

For a full description of the DataFlow::SourceNode API, see the QL JavaScript standard library.

### Exercises¶

Exercise 1: Write a query that finds all hard-coded strings used as the tagName argument to the createElement function from the DOM document object, using local data flow. (Answer).

## Global data flow¶

Global data flow tracks data flow throughout the entire program, and is therefore more powerful than local data flow. However, global data flow is less precise than local data flow. That is, the analysis may report spurious flows that cannot in fact happen. Moreover, global data flow analysis typically requires significantly more time and memory than local analysis.

### Using global data flow¶

For performance reasons, it is not generally feasible to compute all global data flow across the entire program. Instead, you can define a data flow configuration, which specifies source data flow nodes and sink data flow nodes (“sources” and “sinks” for short) of interest. The data flow library provides a generic data flow solver that can check whether there is (global) data flow from a source to a sink.

Optionally, configurations may specify extra data flow edges to be added to the data flow graph, and may also specify barriers. Barriers are data flow nodes or edges through which data should not be tracked for the purposes of this analysis.

To define a configuration, extend the class DataFlow::Configuration as follows:

class MyDataFlowConfiguration extends DataFlow::Configuration {
MyDataFlowConfiguration() { this = "MyDataFlowConfiguration" }

override predicate isSource(DataFlow::Node source) { /* ... */ }

override predicate isSink(DataFlow::Node sink) { /* ... */ }

// optional overrides:
override predicate isBarrier(DataFlow::Node nd) { /* ... */ }
override predicate isBarrierEdge(DataFlow::Node pred, DataFlow::Node succ) { /* ... */ }
override predicate isAdditionalFlowStep(DataFlow::Node pred, DataFlow::Node succ) { /* ... */ }
}


The characteristic predicate MyDataFlowConfiguration() defines the name of the configuration, so "MyDataFlowConfiguration" should be replaced by a suitable name describing your particular analysis configuration.

The data flow analysis is performed using the predicate hasFlow(source, sink):

from MyDataFlowConfiguration dataflow, DataFlow::Node source, DataFlow::Node sink
where dataflow.hasFlow(source, sink)
select source, "Data flow from $@ to$@.", source, source.toString(), sink, sink.toString()


### Using global taint tracking¶

Global taint tracking extends global data flow with additional non-value-preserving steps, such as flow through string-manipulating operations. To use it, simply extend TaintTracking::Configuration instead of DataFlow::Configuration:

class MyTaintTrackingConfiguration extends TaintTracking::Configuration {
MyTaintTrackingConfiguration() { this = "MyTaintTrackingConfiguration" }

override predicate isSource(DataFlow::Node source) { /* ... */ }

override predicate isSink(DataFlow::Node sink) { /* ... */ }
}


Analogous to isAdditionalFlowStep, there is a predicate isAdditionalTaintStep that you can override to specify custom flow steps to consider in the analysis. Instead of the isBarrier and isBarrierEdge predicates, the taint tracking configuration includes isSanitizer and isSanitizerEdge predicates that specify data flow nodes or edges that act as taint sanitizers and hence stop flow from a source to a sink.

Similar to global data flow, the characteristic predicate MyTaintTrackingConfiguration() defines the unique name of the configuration, so "MyTaintTrackingConfiguration" should be replaced by an appropriate descriptive name.

The taint tracking analysis is again performed using the predicate hasFlow(source, sink).

### Examples¶

The following taint-tracking configuration is a generalization of our example query above, which tracks flow from command-line arguments to readFile calls, this time using global taint tracking.

import javascript

class CommandLineFileNameConfiguration extends TaintTracking::Configuration {
CommandLineFileNameConfiguration() { this = "CommandLineFileNameConfiguration" }

override predicate isSource(DataFlow::Node source) {
}

override predicate isSink(DataFlow::Node sink) {
}
}

from CommandLineFileNameConfiguration cfg, DataFlow::Node source, DataFlow::Node sink
where cfg.hasFlow(source, sink)
select source, sink


This query will now find flows that involve inter-procedural steps, like in the following example (where the individual steps have been marked with comments #1 to #4):

const fs = require('fs'),
path = require('path');

p = path.resolve(p);           // #3
'utf8', (err, data) => {
if (err) throw err;
console.log(data);
});
}



Note that for step #3 we rely on the taint-tracking library’s built-in model of the Node.js path library, which adds a taint step from p to path.resolve(p). This step is not value preserving, but it preserves taint in the sense that if p is user-controlled, then so is path.resolve(p) (at least partially).

Other standard taint steps include flow through string-manipulating operations such as concatenation, JSON.parse and JSON.stringify, array transformations, promise operations, and many more.

### Sanitizers¶

The above JavaScript program allows the user to read any file, including sensitive system files like /etc/passwd. If the program may be invoked by an untrusted user, this is undesirable, so we may want to constrain the path. For example, instead of using path.resolve we could implement a function checkPath that first makes the path absolute and then checks that it starts with the current working directory, aborting the program with an error if it does not. We could then use that function in readFileHelper like this:

function readFileHelper(p) {
p = checkPath(p);
...
}


For the purposes of our above analysis, checkPath is a sanitizer: its output is always untainted, even if its input is tainted. To model this we can add an override of isSanitizer to our taint-tracking configuration like this:

class CommandLineFileNameConfiguration extends TaintTracking::Configuration {

// ...

override predicate isSanitizer(DataFlow::Node nd) {
nd.(DataFlow::CallNode).getCalleeName() = "checkPath"
}
}


This says that any call to a function named checkPath is to be considered a sanitizer, so any flow through this node is blocked. In particular, the query would no longer flag the flow from process.argv[2] to fs.readFile in our updated example above.

### Sanitizer guards¶

A perhaps more natural way of implementing the path check in our example would be to have checkPath return a Boolean value indicating whether the path is safe to read (instead of returning the path if it is safe and aborting otherwise). We could then use it in readFileHelper like this:

function readFileHelper(p) {
if (!checkPath(p))
return;
...
}


Note that checkPath is now no longer a sanitizer in the sense described above, since the flow from process.argv[2] to fs.readFile does not go through checkPath any more. The flow is, however, guarded by checkPath in the sense that the expression checkPath(p) has to evaluate to true (or, more precisely, to a truthy value) in order for the flow to happen.

Such sanitizer guards can be supported by defining a new subclass of TaintTracking::SanitizerGuardNode and overriding the predicate isSanitizerGuard in the taint-tracking configuration class to add all instances of this class as sanitizer guards to the configuration.

For our above example, we would begin by defining a subclass of SanitizerGuardNode that identifies guards of the form checkPath(...):

class CheckPathSanitizerGuard extends TaintTracking::SanitizerGuardNode, DataFlow::CallNode {
CheckPathSanitizerGuard() { this.getCalleeName() = "checkPath" }

override predicate sanitizes(boolean outcome, Expr e) {
outcome = true and
e = getArgument(0).asExpr()
}
}


The characteristic predicate of this class checks that the sanitizer guard is a call to a function named checkPath. The overriding definition of sanitizes says such a call sanitizes its first argument (that is, getArgument(0)) if it evaluates to true (or rather, a truthy value).

Now we can override isSanitizerGuard to add these sanitizer guards to our configuration:

class CommandLineFileNameConfiguration extends TaintTracking::Configuration {

// ...

override predicate isSanitizerGuard(TaintTracking::SanitizerGuardNode nd) {
nd instanceof CheckPathSanitizerGuard
}
}


With these two additions, the query recognizes the checkPath(p) check as sanitizing p after the return, since execution can only reach there if checkPath(p) evaluates to a truthy value. Consequently, there is no longer a path from process.argv[2] to readFile.

Sometimes the default data flow and taint steps provided by DataFlow::Configuration and TaintTracking::Configuration are not sufficient and we need to add additional flow or taint steps to our configuration to make it find the expected flow. For example, this can happen because the analyzed program uses a function from an external library whose source code is not available to the analysis, or because it uses a function that is too difficult to analyze.

In the context of our running example, assume that the JavaScript program we are analyzing uses a (fictitious) npm package resolve-symlinks to resolve any symlinks in the path p before passing it to readFile:

const resolveSymlinks = require('resolve-symlinks');

...
}


Resolving symlinks does not make an unsafe path any safer, so we would still like our query to flag this, but since the standard library does not have a model of resolve-symlinks it will no longer return any results.

We can fix this quite easily by adding an overriding definition of the isAdditionalTaintStep predicate to our configuration, introducing an additional taint step from the first argument of resolveSymlinks to its result:

class CommandLineFileNameConfiguration extends TaintTracking::Configuration {

// ...

override predicate isAdditionalTaintStep(DataFlow::Node pred, DataFlow::Node succ) {
exists(DataFlow::CallNode c |
pred = c.getArgument(0) and
succ = c
)
}
}


We might even consider adding this as a default taint step to be used by all taint-tracking configurations. In order to do this, we need to wrap it in a new subclass of TaintTracking::AdditionalTaintStep like this:

class StepThroughResolveSymlinks extends TaintTracking::AdditionalTaintStep, DataFlow::CallNode {

override predicate step(DataFlow::Node pred, DataFlow::Node succ) {
pred = this.getArgument(0) and
succ = this
}
}


If we add this definition to the standard library, it will be picked up by all taint-tracking configurations. Obviously, one has to be careful when adding such new additional taint steps to ensure that they really make sense for all configurations.

Analogous to TaintTracking::AdditionalTaintStep, there is also a class DataFlow::AdditionalFlowStep that can be extended to add extra steps to all data-flow configurations, and hence also to all taint-tracking configurations.

### Exercises¶

Exercise 2: Write a query that finds all hard-coded strings used as the tagName argument to the createElement function from the DOM document object, using global data flow. (Answer).

Exercise 3: Write a class which represents flow sources from the array elements of the result of a call, for example the expression myObject.myMethod(myArgument)[myIndex]. Hint: array indices are properties with numeric names; you can use regular expression matching to check this. (Answer)

Exercise 4: Using the answers from 2 and 3, write a query which finds all global data flows from array elements of the result of a call to the tagName argument to the createElement function. (Answer)

## What next?¶

### Exercise 1¶

import javascript

from DataFlow::CallNode create, string name
where
create = DataFlow::globalVarRef("document").getAMethodCall("createElement") and
create.getArgument(0).mayHaveStringValue(name)
select name


### Exercise 2¶

import javascript

class HardCodedTagNameConfiguration extends DataFlow::Configuration {
HardCodedTagNameConfiguration() { this = "HardCodedTagNameConfiguration" }

override predicate isSource(DataFlow::Node source) { source.asExpr() instanceof ConstantString }

override predicate isSink(DataFlow::Node sink) {
sink = DataFlow::globalVarRef("document").getAMethodCall("createElement").getArgument(0)
}
}

from HardCodedTagNameConfiguration cfg, DataFlow::Node source, DataFlow::Node sink
where cfg.hasFlow(source, sink)
select source, sink


### Exercise 3¶

import javascript

class ArrayEntryCallResult extends DataFlow::Node {
ArrayEntryCallResult() {
exists(DataFlow::CallNode call, string index |
index.regexpMatch("\\d+")
)
}
}


### Exercise 4¶

import javascript

class ArrayEntryCallResult extends DataFlow::Node {
ArrayEntryCallResult() {
exists(DataFlow::CallNode call, string index |
index.regexpMatch("\\d+")
)
}
}

class HardCodedTagNameConfiguration extends DataFlow::Configuration {
HardCodedTagNameConfiguration() { this = "HardCodedTagNameConfiguration" }

override predicate isSource(DataFlow::Node source) { source instanceof ArrayEntryCallResult }

override predicate isSink(DataFlow::Node sink) {
sink = DataFlow::globalVarRef("document").getAMethodCall("createElement").getArgument(0)
}
}

from HardCodedTagNameConfiguration cfg, DataFlow::Node source, DataFlow::Node sink
where cfg.hasFlow(source, sink)
select source, sink