Static application security testing (SAST) examines source code or a generated code representation for security weaknesses without running the application. It can help developers find issues near the code that needs attention, but it cannot identify every vulnerability or replace testing of a running system. Its value depends on what the scanner supports, how it is configured, and how the team reviews its findings.
What SAST analyzes
A SAST scanner applies security rules or analysis techniques to an application’s code without executing the application. Depending on the tool, it may analyze source files directly or use a representation generated from the code. Findings can identify a file, line, or code snippet that warrants investigation. OWASP describes potential examples such as buffer overflows and SQL injection, but coverage varies by tool and configuration. See OWASP’s overview of source code analysis tools.
SAST is often called white-box testing because the analysis can inspect the code itself. That is different from checking only what an application exposes while it runs.
SAST, DAST, and SCA: what is the difference?
| Practice | What it examines | What it can help reveal |
|---|---|---|
| SAST | Source code or a code representation, without running the application | Potential security weaknesses associated with code and its implementation |
| DAST | A running application, by applying input in a test environment | How the application behaves when exercised through its exposed interfaces |
| SCA | Open-source components and their known vulnerabilities | Security risks in dependencies rather than only the application’s own code |
OWASP treats static analysis, dynamic testing, and software composition analysis as distinct practices. DAST exercises an application while it is running; SCA focuses on open-source components. They examine different materials and contexts, so one should not be described as a substitute for the others. The OWASP Developer Guide explains the static-versus-dynamic distinction.
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How a SAST scan fits into development
- Check language and framework support. Confirm that the scanner can analyze the languages and frameworks in the repository.
- Configure analysis inputs. Some tools inspect source files directly; others need a generated representation. Build requirements depend on the scanner and language.
- Run the analysis where developers can act on it. Teams may use an IDE integration, a local command, or a CI pipeline. OWASP notes that SAST tools can be run repeatedly, including as part of CI.
- Review findings in context. Check the affected code and the rule’s reasoning before deciding whether an alert is a real issue, a false positive, or needs further investigation.
- Fix or document the result. Address confirmed issues and make any suppression or rule tuning deliberate, evidence-based, and reviewable.
For a documented example—not a requirement for every scanner—GitHub CodeQL creates a database representation of a codebase and runs queries over it. GitHub documents default and advanced setup, direct CLI use, and custom analysis. For compiled languages, database generation can involve build configuration; supported build modes vary by language. Consult GitHub’s guidance for CodeQL analysis of compiled languages and the CodeQL CLI documentation for the relevant setup.
GitHub code scanning can also ingest findings from third-party tools that produce SARIF, a format for static analysis results. That can make it possible to bring external scanner results into a code-scanning workflow; it does not mean every scanner integrates identically. See GitHub’s documentation on SARIF files for code scanning.
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What SAST can and cannot tell you
Where it helps
- Earlier feedback: A finding tied to a file or line can give a developer a concrete place to start investigating.
- Repeatable checks: Scans can be run throughout development and in CI rather than reserved for a final test phase.
- Broad code review: Automated rules can flag recognizable patterns across a large project, including some well-known issue types such as buffer overflows or SQL injection.
Where it falls short
- Coverage is incomplete. OWASP notes that authentication problems, access-control issues, and insecure cryptography can be difficult to find automatically. A scanner’s results depend on the vulnerability classes its rules can recognize.
- Alerts need triage. SAST tools can produce false positives. An alert is a lead to investigate, not proof by itself that an exploitable vulnerability exists.
- Design and runtime context matter. The archived OWASP Testing Guide says: “Static source code analysis alone cannot identify issues due to flaws in the design, since it cannot understand the context in which the code is constructed.” Static inspection also cannot, by itself, establish how a deployed application behaves.
- Some code or risks may be outside its view. Configuration issues may not be represented in the analyzed code, and tools may have difficulty analyzing code that cannot be compiled.
For these reasons, a scan with no findings is not proof that an application is secure. SAST is one layer in a broader security-testing process, not a security verdict.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose a SAST tool
There is no universally best scanner established by these criteria. Compare tools against the repository and team workflow rather than relying on a broad product ranking. OWASP recommends evaluating factors such as language coverage, accuracy, integration, and licensing in its source code analysis tool guidance.
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- Language and framework coverage: Does it support the project’s actual languages, frameworks, and libraries?
- Finding scope: Which vulnerability classes and standards or taxonomies does it address?
- Precision and triage effort: What evidence is available about false positives and false negatives? How much review work can the team sustain?
- Analysis prerequisites: Does it require buildable source, a particular build setup, or support for binary analysis?
- Workflow fit: Can developers use it in their IDE or local workflow, and can it run reliably in CI/CD?
- Customization and interoperability: Can rules be tuned appropriately, and can findings be exchanged in a format such as SARIF?
- Total licensing cost: Does the licensing model fit the organization and its intended usage?
Understand CodeQL’s query-suite tradeoff
CodeQL illustrates why configuration matters. GitHub documents a default query suite and a broader security-extended suite. The extended suite adds queries at somewhat lower precision and may produce more false positives, so a team should weigh broader coverage against the review load and validate the setup for its repository. Details are in GitHub’s CodeQL query-suite documentation.
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