codeql-lab

codeql-lab is a consolidated Git repository that collects all relevant CodeQL components, resources, and tooling into a single version-controlled location.

Purpose

The goal of this repository is to provide an integrated development environment (“lab”) for CodeQL research, experimentation, and custom query development. It simplifies setup by maintaining all required submodules, configuration files, and datasets in one place.

Repository Location

The primary repository is hosted at: https://github.com/hohn/codeql-lab

Intended Use Cases

Local experimentation with CodeQL queries and libraries.
End-to-end testing of custom model data and query logic. This includes writing and validating custom data flow models, adjusting model coverage, and confirming that query results behave as expected across controlled datasets. The lab setup supports rapid iteration on QL logic, helping detect unintended changes and enabling reproducible evaluations of taint tracking, control flow, or API usage patterns.
Structured collaboration and controlled updates across all CodeQL-related artifacts.
Simplified onboarding and reproducible setup for new contributors or analysis environments.

Prerequisites

Working with this repository assumes prior experience with:

Git, Bash, and standard Unix command-line tools. These are used throughout and are required for setup and day-to-day tasks. Tools such as ripgrep, GNU Bash, and grep/regex workflows are assumed.
At least one supported programming language, such as C, C++, Java, Python, Go, or Ruby. A solid understanding of the target language is necessary to interpret analysis results and write effective queries. See general background on programming languages if needed.
Basic familiarity with program structure concepts, including abstract syntax trees (ASTs), control-flow graphs (CFGs), and data-flow graphs (DFGs). These are core to how CodeQL models code behavior.
Optional but helpful: familiarity with structural or functional programming languages (e.g. Lisp or OCaml) can make working with CodeQL’s query language and type system more intuitive. See overview of functional programming for related context.

Repository Layout

Core Structure

Repository is based on: https://github.com/github/vscode-codeql-starter.git
All development work is done on the branch: qllab

CodeQL version is pinned via the ql/ submodule:

commit 4d681f05bd671f8b5e31624f16a2b4d75e61c071 (tag: codeql-cli/v2.22.0)

A prebuilt CodeQL CLI binary is included:
```
1104625939  assets/codeql-osx64.zip
```
Project-specific repositories can be added directly under the root. Example: the C dataflow workshop in ./codeql-dataflow-sql-injection-c

Additional Structure Notes

The original upstream README.md is preserved at ./README-vscode-codeql-starter.md

Possible Reading Orders

Data Flow

Review: SQLite Injection Workshop, Java

We begin with a recap of the Java-based injection example, focusing on the vulnerable code in AddUser.java. Following that, we examine a fully manual CodeQL query available in full-query.ql, which was written to explicitly trace tainted data through the program. Next, we explore the out-of-the-box query SqlTainted.ql included in the standard CodeQL packs, and conclude with an inspection of the relevant base classes and framework modeling in Illustrations.ql.

start with SqlTainted.ql, note that it won't find our injection
break / comment the pre-done additions in .github/codeql/extensions/sqlite-db/models/sqlite.model.yml

Debugging data flow config (instead of taint flow), Java

We can illustrate taint-flow debugging in the Java SQL injection sample

TODO Debugging data flow config (instead of taint flow), C

A corresponding example for C is planned, using a simplified query to trace value propagation in ~/work-gh/codeql-lab/codeql-dataflow-sql-injection-c/add-user.c. Unlike Java, C may require manual modeling even to visualize basic flows.

C detail
- Dataflow node types vs. AST vs. CFG, but more choices for the C versions: after call, pointer.
  - asDefiningArgument(), asExpr(), asIndirectArgument()
  - asExpr() in C now may cause the path to fail, even though sink and source are found
getAQlClass() to get precise type
ql/actions/ql/src/Debug/partial.ql
ql/cpp/ql/lib/CHANGELOG.md 176:* Deleted the deprecated `explorationLimit` predicate from `DataFlow::Configuration`, use `FlowExploration<explorationLimit>` instead.
codeql-sqlite-java/TaintFlowDebugging.md 54:int explorationLimit() { result = 100 } 58:module MyPartialFlow = MyFlow::FlowExplorationFwd<explorationLimit/0>;
Debugging docs: https://codeql.github.com/docs/writing-codeql-queries/debugging-data-flow-queries-using-partial-flow/#debugging-data-flow-queries-using-partial-flow

Modeling

There are two primary approaches to modeling: direct use of CodeQL predicates and the models-as-data system. The models-as-data system is implemented in QL but relies on external YAML files that are interpreted at query evaluation time.

The model editor provides a GUI for managing YAML-based models, but the underlying format is identical to that used by the models-as-data system. In C and other cases where GUI support is limited or unavailable, we write these YAML models manually and invoke them directly from queries.

When YAML models are written directly, the use of GPT-based tooling becomes very natural. GPTs can extract function signatures, parameter semantics, and flow annotations from documentation or code examples, then generate valid YAML model entries automatically.

XX models-as-data is good for simple but large quantity APIs. For anything complicated, use CodeQL
The CodeQL parser is optimized for reading large CodeQL files. E.g., 14,000 predicates are no problem.
At this scale, you're generating. The type checking you get from CodeQL is much more extensive than models-as-data. models-as-data is text; CodeQL is a type-checked language.

TODO MaD (models as data) resources

https://codeql.github.com/docs/codeql-language-guides/customizing-library-models-for-cpp/ https://docs.github.com/en/code-security/codeql-for-vs-code/using-the-advanced-functionality-of-the-codeql-for-vs-code-extension/using-the-codeql-model-editor#testing-codeql-model-packs-in-vs-code https://docs.github.com/en/code-security/codeql-cli/codeql-cli-manual/database-analyze#--model-packsnamerange examples: https://github.com/github/codeql/blob/main/cpp/ql/lib/ext/Windows.model.yml#L8

documentation for the specific possible values of MaD columns other than the most generic spec can be found here: https://github.com/github/codeql/blob/main/cpp/ql/lib/semmle/code/cpp/dataflow/ExternalFlow.qll#L35 This is covered in more detail in

each language has one of these ExternalFlow lib files and each includes more description on what the potential values actually mean

Modeling overview as diagram

                                    +----------------------+
                                    |     Modeling in      |
                                    |       CodeQL         |
                                    +----------+-----------+
                                               |
                +------------------------------+------------------------------+
                |                                                             |
       +--------v--------+                                          +---------v---------+
       | Direct CodeQL   |                                          |  Models-as-Data   |
       | (QL predicates) |                                          |  (YAML + QL eval) |
       +--------+--------+                                          +---------+---------+
                |                                                             |
                |                                                             |
     +----------v----------+                                  +---------------v---------------+
     | Manual customization|                                  |     YAML models via GUI       |
     | via Customizations.qll                                 |    (Model Editor frontend)    |
     +----------+----------+                                  +---------------+---------------+
                |                                                             |
                |                                                             |
      +---------v---------+                                       +-----------v-----------+
      | Java: built-in     |                                      | Java: Jedis + Console |
      | includes .qll hook |                                      | GUI modeling examples |
      +--------------------+                                      +-----------------------+
                |
                | Manual setup needed for:
                v
       +------------------------+
       |   C / C++: requires    |
       |   cpp.qll patch +      |
       |   Customizations.qll   |
       +------------------------+
                |
                v
  +-------------------------------+
  | Use models-as-data directly   |
  | (YAML only, no editor)        |
  +-------------------------------+
                |
                v
  +-------------------------------+
  | GPT-assisted YAML generation |
  | from docs, code, or examples |
  +-------------------------------+

Review: SQLite Injection Workshop, Java

start with SqlTainted.ql, note that it won't find our injection
break / comment the pre-done additions in .github/codeql/extensions/sqlite-db/models/sqlite.model.yml

Customizations via codeql (Java)

To customize CodeQL for Java, we identify and extend base classes to add custom flow sources and sinks. A general explanation of this approach is available in the file README.org, particularly the section supplement codeql: Add to FlowSource or a subclass. For Java, java.qll includes Customizations.qll, which provides extension points for custom flow modeling – this structure is common across most CodeQL-supported languages, with the notable exception of C. Further details on this customization process can be found in incoming.codeql-customizations-workshop.md.

illustrate what source, sink find using QueryInjectionFlowConfig in SqlInjectionQuery.qll
- sink ok
- no source
find the base class of source, so we know what to extend
import gotcha I used import semmle.code.java.dataflow.FlowSources as Sources class ReadLine extends Sources::RemoteFlowSource { Does this work too or is private better?
Q: how to run all the CWE* queries against some file?
packs at https://github.com/advanced-security/codeql-bundle
- how to run all the CWE* queries against some file? – the codeql database analyze command can take several arguments, including a directory or query spec To get the full options, run 0:$ codeql database analyze -vvvv -h Usage: codeql database analyze [OPTIONS] – <database> [<query|dir|suite|pack>…] Analyze a database, producing meaningful results in the context of the source code. Run a query suite (or some individual queries) against a CodeQL database, producing results, styled as alerts or paths, in SARIF or another interpreted format. This command combines the effect of the codeql database run-queries and codeql database interpret-result
- How do you install/include the CodeQL bundles with the modified Customizations.qll? That part we have not deciphered in detail. the CLI tool at https://github.com/advanced-security/codeql-bundle does this – but it's a black box

Customizations via Model Editor: Jedis Example (Java Redis client)

The Jedis example is a straightforward case with no unexpected behavior. Although the library contains many functions, they follow a simple and repetitive pattern, making it ideal for large-scale modeling. The CodeQL model editor can be used to efficiently define sources and sinks for such cases. A detailed explanation is provided OK in Modeling Jedis as a Dependency in Model Editor, while validation of OK the modeled sink is discussed in Verifying the Modeled Sink. Finally, the query-level usage of these models can be seen OK in Identify usage of injection-related models in existing queries.

Customizations via Model Editor: Single-function case (Java SQLite sample)

We extend the Java SQLite example using the model editor, with both the necessary data and specification already available. This example highlights a subtle issue with the model editor: the method java.io.Console.readLine() is already modeled as a taint step and therefore does not appear in the editor interface, even though we need it modeled as a source. This requires special handling. The relevant extensions are defined in ./.github/codeql/extensions/sqlite-db/codeql-pack.yml, and the extension data is provided in ./.github/codeql/extensions/sqlite-db/models/sqlite.model.yml. A detailed OK explanation is available in Using sqlite to illustrate models-as-data.

To support this, we explain how the "models-as-data" system works internally. A diagnostic query can be used to enumerate currently recognized sources and sinks. From there, the relevant entry points – such as QL classes and predicates – can be identified by inspecting representative queries like SqlTainted.ql.

find existing readline modling

  hohn@ghm3 ~/work-gh/codeql-lab
  0:$ rg -il 'readline' ql/java --type=yaml
  ql/java/ql/lib/ext/com.google.common.io.model.yml
  ql/java/ql/lib/ext/org.apache.cxf.helpers.model.yml
  ql/java/ql/lib/ext/java.io.model.yml
  ql/java/ql/lib/ext/generated/java.io.model.yml
  ql/java/ql/lib/ext/generated/kotlinstdlib.model.yml
  ql/java/ql/lib/ext/generated/jenkins.model.yml
  ql/java/ql/lib/ext/generated/org.apache.commons.io.model.yml
  ql/java/ql/lib/ext/experimental/com.google.common.io.model.yml

Review: SQLite Injection Workshop (C)

This is the C version of the injection workshop, based on ./codeql-dataflow-sql-injection-c/add-user.c. It serves as the basis for both the "models-as-data" manual modeling and the extension via Customizations.qll.

(PARTIAL) Use models-as-data QL code directly (no graphical editor)

This section focuses on using the models-as-data system without the graphical model editor. While model definition files and supporting data already exist, we manually write YAML files to add or override flow behavior. This approach is especially relevant for C, where graphical tooling is limited or nonexistent.

As reinforcement, we reuse the C version of the SQLite injection workshop:

The code sample is at ~/work-gh/codeql-lab/codeql-dataflow-sql-injection-c/add-user.c.
The accompanying query is ~/work-gh/codeql-lab/codeql-dataflow-sql-injection-c/SqlInjection.ql.

For structural reference, see the Java version’s documentation (not the editor interface): Using sqlite to illustrate models-as-data. There is no separate C-specific walkthrough because the YAML structure and logic are nearly identical.

For workshop use, we extend the example by modeling key functions manually:

Add a source model for: count = read(STDIN_FILENO, buf, BUFSIZE);
Add a sink model for: rc = sqlite3_exec(db, query, NULL, 0, &zErrMsg);

We demonstrate how to define YAML-based models for standard functions like read() and verify their effect using the out-of-the-box query: SqlTainted.ql.

As an additional teaching case, we introduce the higher-level, redundant function char* get_user_info() as a custom source—even though it internally calls a function already modeled as a source—to illustrate how user-defined extensions affect propagation logic.

(PARTIAL) Extending Queries with Customizations.qll for C

The manual YAML modeling approach described earlier works well for isolated or prototype cases. However, for idiomatic, large-scale, or reusable CodeQL analysis, it is often preferable to define custom dataflow logic directly in QL—using Customizations.qll.

Most CodeQL-supported languages (e.g., Java, Python) include built-in support for this mechanism. For example, Java’s primary entry point java.qll automatically imports Customizations.qll, exposing extension points for user-defined sources, sinks, and flow steps.

In contrast, C and C++ do not support this out of the box. To enable it, you must manually patch the language pack and (optionally) rebuild the CodeQL bundle.

This section is partially complete: we document the required source-level QL changes, but the bundling process is still pending.

To enable Customizations.qll support for C/C++, perform the following:

Modify ql/cpp/ql/lib/cpp.qll to import your Customizations.qll module.
Create and populate ql/cpp/ql/lib/Customizations.qll with new source/sink/flow logic.
For full deployment: Rebuild the CodeQL bundle to include the updated QL files.
- This allows portable use in CLI runs and IDE workflows.
- Once bundled, C/C++ customization behaves like any other supported language.
For workshops and local development: No bundling is needed.
- If you run queries directly from the modified source tree, the changes take effect immediately.

A working demonstration of this modification (without bundling) is provided in: ./codeql-dataflow-sql-injection-c/README.org

same workflow as Java: extend RemoteFlowSource, do it in Customizations.qll to affect all queries.
model pack existence has to be explicitly specified

Options to control the model packs to be used

  --model-packs=<name@range>...
  A list of CodeQL pack names, each with an optional version range, to be used as model packs to customize the queries that are about to be evaluated.

TODO CodeQL Bundling

This section will provide a detailed walkthrough of the CodeQL bundling process using the CLI tool at https://github.com/advanced-security/codeql-bundle. This tool enables custom pack composition and is necessary when extending language libraries (e.g., adding `Customizations.qll` support for C/C++).

While the official tool is somewhat of a black box, we will demystify the underlying structure and show how to build, inspect, and deploy custom bundles from source. Notes and scripts will be collected in codeql-bundling/README.org::XX: continue.

CodeQL bundle info:

original bundles found at: https://github.com/github/codeql-action/releases/tag/codeql-bundle-v2.22.2
custom bundler found: https://github.com/advanced-security/codeql-bundle?tab=readme-ov-file#codeql-customization-packs
use a Customizations.qll pack (https://github.com/advanced-security/codeql-bundle?tab=readme-ov-file#codeql-customization-packs these do get created as a separate pack from the rest of the lib)
This part of the custom bundle tool documentation (https://github.com/advanced-security/codeql-bundle/blob/main/codeql_bundle/helpers/bundle.py#L329) explains how the tool reverses dependencies: the built-in libraries are modified to depend on the custom library. The custom bundle must include a file named Customizations (a convention enforced by the bundler), but it can also contain additional libraries with arbitrary names.
Generating CodeQL Models for API Endpoints

To support automatic generation of API endpoint models in a CodeQL workshop (without using the model editor), you can leverage the existing infrastructure used in the cpp/ql/lib/ext/generated directory of the CodeQL repo:
File Generation

Individual model files are generated using the following script: https://github.com/github/codeql/blob/main/misc/scripts/models-as-data/generate_mad.py

Bulk Generation
For batch processing, use: https://github.com/github/codeql/blob/main/misc/scripts/models-as-data/bulk_generate_mad.py

This script requires:
- A language-specific YAML config file — for C++: https://github.com/github/codeql/blob/main/cpp/bulk_generation_targets.yml
- A DCA run (Data-Collection Analysis) to provide the necessary input data.
These tools allow you to programmatically produce model files similar to those found in ql/lib/ext/generated, making them suitable for automated or instructional use cases.
Updated MAD Generator (no more DCA step)

The script `generate_mad.py` replaces the older DCA-based workflow. It runs a set of language-specific CodeQL queries directly against a database and emits `.model.yml` files.
- Queries used:
  - CaptureSummaryModels.ql
  - CaptureSinkModels.ql
  - CaptureSourceModels.ql
  - CaptureNeutralModels.ql
  - CaptureTypeBasedSummaryModels.ql (optional)
- These queries are located in: <language>/ql/src/utils/modelgenerator/
- Output files are written to: <language>/ql/lib/ext/generated/<folder>/*.model.yml
- Example usage:
```
  python3 generate_mad.py --language cpp /path/to/db --with-sinks --with-sources --with-summaries
```
There is no longer any need for intermediate `.dca.json` files or a "DCA run".

A compact shell script illustrating the steps is in ./models-as-data/generate-mad-core
A compact shell/csvtk script illustrating the steps is in ./models-as-data/generate-mad-core.csvtk brew install csvtk
A compact shell/xan script illustrating the steps is in ./models-as-data/generate-mad-core.xan brew install xan https://github.com/github/codeql/tree/main/misc/scripts/models-as-data
bundling semantics good
- pack a_1
  - depends b_1
  - depends b_2
    - depends java-all
good
- pack a_1
  - depends b_1
  - depends b_2
    - depends java-all
      - depends my-custom
cycle, actual current situation. OK for libraries, not packs? Is this import hierarchy
- pack a_1
  - depends b_1
  - depends b_2
    - depends java-all
      - depends my-custom
        
        depends java-all
turned into?
- pack a_1
  - depends b_1
  - depends b_2
    - depends java-all-custom precompiled
The fundamental distinction: Customizations.qll can insert under the stdlib. Other packs are on top of the stdlib.

There is a transient dependency inserted. See codeql-bundle/codeql_bundle/helpers/bundle.py

Tool Setup

Some scripts are used here, found in ./bin/. To ensure the ones written in Python have access to prerequites, set up a virtual environment via

  # 1. Create the virtualenv
  python3 -m venv ~/codeql-lab/venv

  # 2. Install any packages
  source ~/codeql-lab/venv/bin/activate
  pip install pyyaml

For any of these scripts to work, add them to the PATH via

  export PATH="$HOME/codeql-lab/bin:$PATH"

Languages

CodeQL 46.8%

Shell 28.9%

C 10.9%

Python 7.3%

Java 6.1%

README.org Unescape Escape

codeql-lab: Centralized Git Repository for CodeQL Development

Overview

Purpose

Repository Location

Intended Use Cases

Prerequisites

Repository Layout

Core Structure

Additional Structure Notes

Possible Reading Orders

Data Flow

Review: SQLite Injection Workshop, Java

Debugging data flow config (instead of taint flow), Java

TODO Debugging data flow config (instead of taint flow), C

Modeling

TODO MaD (models as data) resources

Modeling overview as diagram

Review: SQLite Injection Workshop, Java

Customizations via codeql (Java)

Customizations via Model Editor: Jedis Example (Java Redis client)

Customizations via Model Editor: Single-function case (Java SQLite sample)

Review: SQLite Injection Workshop (C)

(PARTIAL) Use models-as-data QL code directly (no graphical editor)

(PARTIAL) Extending Queries with Customizations.qll for C

TODO CodeQL Bundling

Tool Setup

README.org