About CodeQL¶

CodeQL is a language and toolchain for code analysis. It is designed to allow security researchers to scale their knowledge of a single vulnerability to identify variants of that vulnerability across a wide range of codebases. It is also designed to allow developers to automate security checks and integrate them into their development workflows.

Resources for learning CodeQL¶

CodeQL docs site: contains information on the CodeQL language and libraries, with tutorials and guides to help you learn how to write your own queries.
- CodeQL queries: A general, language-neutral overview of the key components of a query.
- QL tutorials: Solve puzzles to learn the basics of QL before you analyze code with CodeQL. The tutorials teach you how to write queries and introduce you to key logic concepts along the way.
- CodeQL language guides: Guides to the CodeQL libraries for each language, including the classes and predicates that are available for use in queries, with worked examples.
GitHub Security Lab: is GitHub’s own security research team. They’ve created a range of resources to help you learn how to use CodeQL to find security vulnerabilities in real-world codebases.
- Secure code game: A series of interactive sessions that guide you from finding insecure code patterns manually, through to using CodeQL to find insecure code patterns automatically.
- Security Lab CTF: A series of Capture the Flag (CTF) challenges that are designed to help you learn how to use CodeQL to find security vulnerabilities in real-world codebases.
- Security Lab blog: A series of blog posts that describe how CodeQL is used by security researchers to find security vulnerabilities in real-world codebases.

About variant analysis¶

Variant analysis is the process of using a known security vulnerability as a seed to find similar problems in your code. It’s a technique that security engineers use to identify potential vulnerabilities, and ensure these threats are properly fixed across multiple codebases.

Querying code using CodeQL is the most efficient way to perform variant analysis. You can use the standard CodeQL queries to identify seed vulnerabilities, or find new vulnerabilities by writing your own custom CodeQL queries. Then, develop or iterate over the query to automatically find logical variants of the same bug that could be missed using traditional manual techniques.

When you have a query that finds variants of a vulnerability, you can use multi-repository variant analysis to run that query across a large number of codebases, and identify all of the places where that vulnerability exists. For more information, see Running CodeQL queries at scale with multi-repository variant analysis in the GitHub docs.

CodeQL analysis¶

CodeQL analysis consists of three steps:

Preparing the code, by creating a CodeQL database
Running CodeQL queries against the database
Interpreting the query results

For information on the CodeQL toolchain and on running CodeQL to analyze a codebase, see the CodeQL CLI, CodeQL for Visual Studio Code, and About code scanning with CodeQL in the GitHub docs.

Database creation¶

To create a database, CodeQL first extracts a single relational representation of each source file in the codebase.

For compiled languages, extraction works by monitoring the normal build process. Each time a compiler is invoked to process a source file, a copy of that file is made, and all relevant information about the source code is collected. This includes syntactic data about the abstract syntax tree and semantic data about name binding and type information.

For interpreted languages, the extractor runs directly on the source code, resolving dependencies to give an accurate representation of the codebase.

There is one extractor for each language supported by CodeQL to ensure that the extraction process is as accurate as possible. For multi-language codebases, databases are generated one language at a time.

After extraction, all the data required for analysis (relational data, copied source files, and a language-specific database schema, which specifies the mutual relations in the data) is imported into a single directory, known as a CodeQL database.

Query execution¶

After you’ve created a CodeQL database, one or more queries are executed against it. CodeQL queries are written in a specially-designed object-oriented query language called QL. You can run the queries checked out from the CodeQL repo (or custom queries that you’ve written yourself) using the CodeQL for VS Code extension or the CodeQL CLI. For more information about queries, see “About CodeQL queries.”

Query results¶

The final step converts results produced during query execution into a form that is more meaningful in the context of the source code. That is, the results are interpreted in a way that highlights the potential issue that the queries are designed to find.

Queries contain metadata properties that indicate how the results should be interpreted. For instance, some queries display a simple message at a single location in the code. Others display a series of locations that represent steps along a data-flow or control-flow path, along with a message explaining the significance of the result. Queries that don’t have metadata are not interpreted—their results are output as a table and not displayed in the source code.

Following interpretation, results are output for code review and triaging. In CodeQL for Visual Studio Code, interpreted query results are automatically displayed in the source code. Results generated by the CodeQL CLI can be output into a number of different formats for use with different tools.

About CodeQL databases¶

CodeQL databases contain queryable data extracted from a codebase, for a single language at a particular point in time. The database contains a full, hierarchical representation of the code, including a representation of the abstract syntax tree, the data flow graph, and the control flow graph.

Each language has its own unique database schema that defines the relations used to create a database. The schema provides an interface between the initial lexical analysis during the extraction process, and the actual complex analysis using CodeQL. The schema specifies, for instance, that there is a table for every language construct.

For each language, the CodeQL libraries define classes to provide a layer of abstraction over the database tables. This provides an object-oriented view of the data which makes it easier to write queries.

For example, in a CodeQL database for a Java program, two key tables are:

The expressions table containing a row for every single expression in the source code that was analyzed during the build process.
The statements table containing a row for every single statement in the source code that was analyzed during the build process.

The CodeQL library defines classes to provide a layer of abstraction over each of these tables (and the related auxiliary tables): Expr and Stmt.