Configure custom CodeQL query packs for organization-specific risks
Implementation Effort: High – Authoring and maintaining custom CodeQL packs is an ongoing program that requires learning the QL language, encoding organization-specific rules, and updating queries as internal frameworks evolve. User Impact: Low – Custom queries are written and published by the security team and run automatically in scanning; end users are not prompted or affected. Lifecycle Stage: Code
Overview
Configure custom CodeQL query packs to detect organization-specific security risks that default query suites do not cover. CodeQL ships with a comprehensive set of default queries that detect common vulnerability classes such as SQL injection, cross-site scripting, and insecure deserialization.
These queries are maintained by GitHub's security research team and target patterns found broadly across open-source and enterprise codebases. However, every organization has its own internal APIs, proprietary frameworks, custom authentication helpers, and domain-specific coding conventions that the default query set was never designed to evaluate. A financial services company wrapping sensitive operations in a custom authorization decorator, for example, needs a query that verifies every call site uses that decorator correctly — something no generic query pack will ever check.
Custom CodeQL query packs let security teams encode organization-specific knowledge into the same analysis engine that runs on every pull request. Queries can target internal API misuse, enforce mandatory input validation on proprietary data types, detect deprecated internal library calls, or flag patterns that have caused incidents in the past. A common and high-value use is detecting insecure anti-patterns where developers route around approved frameworks — for example, code that implements custom authentication or token handling instead of using the Microsoft Authentication Library (MSAL), or that uses banned cryptographic primitives. Encoding these as queries supports Microsoft Security Development Lifecycle practice 2, "Require use of proven security features, languages, and frameworks," and feeds the bypass-detection program.
In GitHub, custom packs are published to the GitHub Container registry and referenced in CodeQL configuration files so they run alongside the default suite. In Azure DevOps, custom query packs can be specified in the AdvancedSecurity-Codeql-Init pipeline task through a configuration file. Because findings appear in the same code scanning alert interface developers already use, custom queries integrate into existing triage and remediation workflows without friction.
Without custom queries, the gap between what generic analysis covers and what your specific codebase actually requires remains an open risk. Teams that know they have internal frameworks, proprietary trust boundaries, or recurring incident patterns should encode those checks directly into CodeQL rather than relying only on the default suites. By verifying that code adheres to approved frameworks and organization-specific security rules on every pull request, this task supports Verify explicitly.
Building custom CodeQL queries requires investment in upskilling. CodeQL uses its own purpose-built query language (QL), and teams should plan for a learning curve before expecting production-quality custom queries. GitHub provides CodeQL training resources, and the CodeQL for Visual Studio Code extension offers an interactive development environment for writing, testing, and debugging queries locally. Organizations should start by evaluating community-contributed query packs and extending existing queries before authoring entirely new ones — this builds team familiarity with QL's dataflow and taint-tracking libraries while delivering incremental security value.