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wip: dataflow doc rearrangements
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Michael Hohn
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=Michael Hohn
=Michael Hohn
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@@ -6,7 +6,9 @@
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- [Setup instructions](#setup-instructions)
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- [Documentation links](#documentation-links)
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- [Problem statement](#problem-statement)
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- [Tutorial, part 1: sources and sinks](#tutorial-part-1-sources-and-sinks)
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- [Tutorial, part 1: running the code to see the problem](#tutorial-part-1-running-the-code-to-see-the-problem)
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- [Data flow overview and illustration](#data-flow-overview-and-illustration)
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- [Tutorial, part 3: recap, sources and sinks](#tutorial-part-3-recap-sources-and-sinks)
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- [Codeql recap](#codeql-recap)
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- [Call to SQL query execution (the data sink)](#call-to-sql-query-execution-the-data-sink)
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- [Non-constant query strings and untrusted data (the data source)](#non-constant-query-strings-and-untrusted-data-the-data-source)
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@@ -230,7 +232,47 @@ Looking ahead, we now *know* that there is unsafe external data (source)
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which reaches (flow path) a database-writing command (sink). Thus, a query
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written against this code should find at least one taint flow path.
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## Tutorial, part 1: sources and sinks
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## Data flow overview and illustration
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In the previous sections we identified the sources of problematic strings
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(accesses of `info` etc.), and the sink that their data may flow to (the argument
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to `sqlite3_exec`).
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We need to see if there is data flow between the source(s) and this sink.
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The solution here is to use the data flow library. Data flow is, as the name
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suggests, about tracking the flow of data through the program. It helps answers
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questions like: does this expression ever hold a value that originates from a
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particular other place in the program?
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We can visualize the data flow problem as one of finding paths through a directed
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graph, where the nodes of the graph are elements in program, and the edges
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represent the flow of data between those elements. If a path exists, then the data
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flows between those two nodes.
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This graph represents the flow of data from the tainted parameter. The nodes of
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graph represent program elements that have a value, such as function parameters
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and expressions. The edges of this graph represent flow through these nodes.
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There are two variants of data flow available in CodeQL:
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- Local (“intra-procedural”) data flow models flow within one function; feasible
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to compute for all functions in a CodeQL database.
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- Global (“inter-procedural”) data flow models flow across function calls; not
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feasible to compute for all functions in a CodeQL database.
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While local data flow is feasible to compute for all functions in a CodeQL
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database, global data flow is not. This is because the number of paths becomes
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_exponentially_ larger for global data flow.
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The global data flow (and taint tracking) library avoids this problem by requiring
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that the query author specifies which _sources_ and _sinks_ are applicable. This
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allows the implementation to compute paths only between the restricted set of
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nodes, rather than for the full graph.
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To illustrate the dataflow for this problem, we have a [collection of slides](https://drive.google.com/file/d/1eEG0eGVDVEQh0C-0_4UIMcD23AWwnGtV/view?usp=sharing)
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for this workshop.
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## Tutorial, part 3: recap, sources and sinks
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XX:
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<!--
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!-- The complete project can be downloaded via this
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!-- [drive](https://drive.google.com/file/d/1-6c3S-e4FKa_IsuuzhhXupiAwCzzPgD-/view?usp=sharing)
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@@ -357,58 +399,8 @@ Note that our query structure will extend to more complex cases lateron; only th
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source identification will need updating.
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## Data flow overview
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In the previous sections we identified the sources of problematic strings
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(accesses of `iUUID` etc.), and the sink that their data may flow to (the argument
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to `executeStatement`)
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We need to see if there is data flow between the source(s) and this sink.
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The solution here is to use the data flow library. Data flow is, as the name
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suggests, about tracking the flow of data through the program. It helps answers
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questions like: does this expression ever hold a value that originates from a
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particular other place in the program?
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We can visualize the data flow problem as one of finding paths through a directed
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graph, where the nodes of the graph are elements in program, and the edges
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represent the flow of data between those elements. If a path exists, then the data
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flows between those two nodes.
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Consider this example C function:
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```c
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int func(int tainted) {
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int x = tainted;
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if (someCondition) {
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int y = x;
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callFoo(y);
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} else {
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return x;
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}
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return -1;
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}
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```
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The data flow graph for this function will look something like this:
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<img src="https://help.semmle.com/QL/ql-training/_images/graphviz-2ad90ce0f4b6f3f315f2caf0dd8753fbba789a14.png" alt="drawing" width="300"/>
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This graph represents the flow of data from the tainted parameter. The nodes of
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graph represent program elements that have a value, such as function parameters
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and expressions. The edges of this graph represent flow through these nodes.
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There are two variants of data flow available in CodeQL:
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- Local (“intra-procedural”) data flow models flow within one function; feasible
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to compute for all functions in a CodeQL database.
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- Global (“inter-procedural”) data flow models flow across function calls; not
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feasible to compute for all functions in a CodeQL database.
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While local data flow is feasible to compute for all functions in a CodeQL
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database, global data flow is not. This is because the number of paths becomes
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_exponentially_ larger for global data flow.
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The global data flow (and taint tracking) library avoids this problem by requiring
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that the query author specifies which _sources_ and _sinks_ are applicable. This
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allows the implementation to compute paths only between the restricted set of
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nodes, rather than for the full graph.
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To use global data flow and taint tracking we need to
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- a taint flow configuration
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